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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 31 Oct 2013 11:55:23 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Oct/31/t1383235013nbhjuiq61drpt6r.htm/, Retrieved Mon, 29 Apr 2024 03:07:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=221546, Retrieved Mon, 29 Apr 2024 03:07:32 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact68

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
7	41	38	14	12	3
5	39	32	18	11	5
5	30	35	11	14	4
5	31	33	12	12	4
8	34	37	16	21	5
6	35	29	18	12	5
5	39	31	14	22	2
6	34	36	14	11	5
5	36	35	15	10	4
4	37	38	15	13	4
6	38	31	17	10	5
5	36	34	19	8	3
5	38	35	10	15	5
6	39	38	16	14	3
7	33	37	18	10	5
6	32	33	14	14	3
7	36	32	14	14	4
6	38	38	17	11	5
8	39	38	14	10	4
7	32	32	16	13	3
5	32	33	18	7	4
5	31	31	11	14	4
7	39	38	14	12	3
7	37	39	12	14	3
5	39	32	17	11	4
4	41	32	9	9	5
10	36	35	16	11	4
6	33	37	14	15	4
5	33	33	15	14	4
5	34	33	11	13	4
5	31	28	16	9	4
5	27	32	13	15	3
6	37	31	17	10	4
5	34	37	15	11	5
5	34	30	14	13	4
5	32	33	16	8	4
5	29	31	9	20	3
5	36	33	15	12	4
5	29	31	17	10	4
5	35	33	13	10	4
5	37	32	15	9	5
7	34	33	16	14	4
5	38	32	16	8	3
6	35	33	12	14	3
7	38	28	12	11	4
7	37	35	11	13	4
5	38	39	15	9	4
5	33	34	15	11	5
4	36	38	17	15	4
5	38	32	13	11	5
4	32	38	16	10	4
5	32	30	14	14	4
5	32	33	11	18	4
7	34	38	12	14	4
5	32	32	12	11	4
5	37	32	15	12	5
6	39	34	16	13	4
4	29	34	15	9	4
6	37	36	12	10	4
6	35	34	12	15	4
5	30	28	8	20	3
7	38	34	13	12	4
6	34	35	11	12	5
8	31	35	14	14	1
7	34	31	15	13	3
5	35	37	10	11	5
6	36	35	11	17	4
6	30	27	12	12	4
5	39	40	15	13	3
5	35	37	15	14	4
5	38	36	14	13	4
5	31	38	16	15	3
4	34	39	15	13	5
6	38	41	15	10	4
6	34	27	13	11	5
6	39	30	12	19	4
6	37	37	17	13	4
7	34	31	13	17	4
5	28	31	15	13	4
7	37	27	13	9	3
6	33	36	15	11	5
5	37	38	16	10	NA
5	35	37	15	9	5
4	37	33	16	12	4
8	32	34	15	12	4
8	33	31	14	13	5
5	38	39	15	13	4
5	33	34	14	12	4
6	29	32	13	15	3
4	33	33	7	22	4
5	31	36	17	13	4
5	36	32	13	15	3
5	35	41	15	13	5
5	32	28	14	15	5
6	29	30	13	10	5
6	39	36	16	11	4
5	37	35	12	16	4
6	35	31	14	11	4
5	37	34	17	11	4
7	32	36	15	10	4
5	38	36	17	10	4
6	37	35	12	16	4
6	36	37	16	12	5
6	32	28	11	11	4
4	33	39	15	16	4
5	40	32	9	19	3
5	38	35	16	11	5
7	41	39	15	16	4
6	36	35	10	15	3
9	43	42	10	24	2
6	30	34	15	14	5
6	31	33	11	15	4
5	32	41	13	11	5
6	32	33	14	15	1
5	37	34	18	12	5
8	37	32	16	10	5
7	33	40	14	14	3
5	34	40	14	13	4
7	33	35	14	9	5
6	38	36	14	15	5
6	33	37	12	15	3
9	31	27	14	14	4
7	38	39	15	11	5
6	37	38	15	8	4
5	33	31	15	11	4
5	31	33	13	11	4
6	39	32	17	8	5
6	44	39	17	10	4
7	33	36	19	11	5
5	35	33	15	13	4
5	32	33	13	11	4
5	28	32	9	20	4
6	40	37	15	10	4
4	27	30	15	15	3
5	37	38	15	12	4
7	32	29	16	14	5
5	28	22	11	23	3
7	34	35	14	14	4
7	30	35	11	16	3
6	35	34	15	11	4
5	31	35	13	12	3
8	32	34	15	10	3
5	30	34	16	14	5
5	30	35	14	12	5
5	31	23	15	12	5
6	40	31	16	11	5
4	32	27	16	12	5
5	36	36	11	13	4
5	32	31	12	11	4
7	35	32	9	19	4
6	38	39	16	12	5
7	42	37	13	17	5
10	34	38	16	9	4
6	35	39	12	12	4
8	35	34	9	19	4
4	33	31	13	18	5
5	36	32	13	15	3
6	32	37	14	14	4
7	33	36	19	11	5
7	34	32	13	9	5
6	32	35	12	18	5
6	34	36	13	16	5

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'George Udny Yule' @ yule.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=221546&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=221546&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=221546&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 time9 seconds
R Server'George Udny Yule' @ yule.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Multiple Linear Regression - Estimated Regression Equation
Y_t [t] = + 4.73467 -0.0682697X_1t[t] + 0.0129274X_2t[t] -0.00886996X_3t[t] + 0.0291562X_4t[t] -0.0642349X_5t[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y_t

[t] =  +  4.73467 -0.0682697X_1t[t] +  0.0129274X_2t[t] -0.00886996X_3t[t] +  0.0291562X_4t[t] -0.0642349X_5t[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=221546&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y_t

[t] =  +  4.73467 -0.0682697X_1t[t] +  0.0129274X_2t[t] -0.00886996X_3t[t] +  0.0291562X_4t[t] -0.0642349X_5t[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=221546&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=221546&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
Y_t [t] = + 4.73467 -0.0682697X_1t[t] + 0.0129274X_2t[t] -0.00886996X_3t[t] + 0.0291562X_4t[t] -0.0642349X_5t[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.734670.9527064.971.75671e-068.78353e-07
X_1t-0.06826970.0526053-1.2980.1962940.098147
X_2t0.01292740.01940870.66610.5063610.253181
X_3t-0.008869960.0183788-0.48260.6300470.315024
X_4t0.02915620.03095480.94190.3477120.173856
X_5t-0.06423490.0227051-2.8290.005285760.00264288

\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) & 4.73467 & 0.952706 & 4.97 & 1.75671e-06 & 8.78353e-07 \tabularnewline
X_1t & -0.0682697 & 0.0526053 & -1.298 & 0.196294 & 0.098147 \tabularnewline
X_2t & 0.0129274 & 0.0194087 & 0.6661 & 0.506361 & 0.253181 \tabularnewline
X_3t & -0.00886996 & 0.0183788 & -0.4826 & 0.630047 & 0.315024 \tabularnewline
X_4t & 0.0291562 & 0.0309548 & 0.9419 & 0.347712 & 0.173856 \tabularnewline
X_5t & -0.0642349 & 0.0227051 & -2.829 & 0.00528576 & 0.00264288 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=221546&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]4.73467[/C][C]0.952706[/C][C]4.97[/C][C]1.75671e-06[/C][C]8.78353e-07[/C][/ROW]
[ROW][C]X_1t[/C][C]-0.0682697[/C][C]0.0526053[/C][C]-1.298[/C][C]0.196294[/C][C]0.098147[/C][/ROW]
[ROW][C]X_2t[/C][C]0.0129274[/C][C]0.0194087[/C][C]0.6661[/C][C]0.506361[/C][C]0.253181[/C][/ROW]
[ROW][C]X_3t[/C][C]-0.00886996[/C][C]0.0183788[/C][C]-0.4826[/C][C]0.630047[/C][C]0.315024[/C][/ROW]
[ROW][C]X_4t[/C][C]0.0291562[/C][C]0.0309548[/C][C]0.9419[/C][C]0.347712[/C][C]0.173856[/C][/ROW]
[ROW][C]X_5t[/C][C]-0.0642349[/C][C]0.0227051[/C][C]-2.829[/C][C]0.00528576[/C][C]0.00264288[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=221546&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=221546&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)4.734670.9527064.971.75671e-068.78353e-07
X_1t-0.06826970.0526053-1.2980.1962940.098147
X_2t0.01292740.01940870.66610.5063610.253181
X_3t-0.008869960.0183788-0.48260.6300470.315024
X_4t0.02915620.03095480.94190.3477120.173856
X_5t-0.06423490.0227051-2.8290.005285760.00264288






Multiple Linear Regression - Regression Statistics
Multiple R0.334264
R-squared0.111732
Adjusted R-squared0.0830785
F-TEST (value)3.89939
F-TEST (DF numerator)5
F-TEST (DF denominator)155
p-value0.00234046
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.762219
Sum Squared Residuals90.0516

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.334264 \tabularnewline
R-squared & 0.111732 \tabularnewline
Adjusted R-squared & 0.0830785 \tabularnewline
F-TEST (value) & 3.89939 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 155 \tabularnewline
p-value & 0.00234046 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.762219 \tabularnewline
Sum Squared Residuals & 90.0516 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=221546&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.334264[/C][/ROW]
[ROW][C]R-squared[/C][C]0.111732[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0830785[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.89939[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]155[/C][/ROW]
[ROW][C]p-value[/C][C]0.00234046[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.762219[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]90.0516[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=221546&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=221546&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 R0.334264
R-squared0.111732
Adjusted R-squared0.0830785
F-TEST (value)3.89939
F-TEST (DF numerator)5
F-TEST (DF denominator)155
p-value0.00234046
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.762219
Sum Squared Residuals90.0516






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
134.08712-1.08712
254.431880.568117
343.892130.107872
444.08042-0.0804217
553.417431.58257
654.274280.725722
723.61754-1.61754
854.146870.853128
944.34326-0.343257
1044.20514-0.20514
1154.394630.605366
1234.59722-1.59722
1353.902161.09784
1434.05938-1.05938
1554.237660.762336
1633.95492-0.954922
1743.947230.0527681
1854.268310.73169
1944.12146-0.121464
2034.01807-1.01807
2144.58946-0.589461
2243.940540.0594644
2334.06126-1.06126
2433.83976-0.839757
2544.40273-0.402727
2654.392070.607929
2743.966830.03317
2843.868130.131865
2944.06528-0.0652752
3044.02581-0.0258128
3144.4341-0.434101
3233.87403-0.874034
3344.38171-0.381707
3454.235430.764573
3544.13989-0.139891
3644.46691-0.466913
3733.47096-0.470959
3844.23253-0.232527
3944.34656-0.346557
4044.28976-0.289757
4154.447030.552971
4243.970820.0291806
4334.55335-1.55335
4433.93539-0.935392
4544.14296-0.142959
4643.910320.0896842
4744.39787-0.397867
4854.249110.75089
4944.12205-0.122055
5054.273170.726826
5144.36236-0.362363
5244.0498-0.0498015
5343.678780.321216
5443.809840.190155
5544.16645-0.166454
5654.254320.745675
5744.15909-0.159091
5844.39414-0.39414
5944.19158-0.191576
6043.862290.137713
6133.48134-0.48134
6244.05466-0.0546603
6354.004040.995962
6413.78772-2.78772
6534.02364-1.02364
6654.102570.897426
6743.708720.291282
6844.05244-0.0524444
6934.14498-1.14498
7044.05565-0.0556502
7144.13838-0.138381
7233.95999-0.959992
7354.157490.842513
7444.24762-0.247622
7554.197540.802455
7643.692540.307463
7744.13578-0.135783
7843.708390.291614
7944.08261-0.082613
8034.29653-1.29653
8154.16310.8369
82NANA0.623175
8355.34288-0.34288
8443.967140.0328614
8542.913281.08672
8655.14093-0.140927
8744.15572-0.155719
8844.83162-0.831619
8932.386420.613584
9044.13536-0.135358
9144.99038-0.99038
9232.084410.915595
9354.003310.996693
9454.170530.829467
9555.26982-0.269821
9643.883310.116693
9744.20415-0.204149
9844.35913-0.359132
9944.14614-0.146138
10044.41855-0.418554
10143.815040.184963
10243.157930.842066
10355.10451-0.104508
10443.951860.0481447
10544.66853-0.668525
10632.334030.665967
10754.850470.149534
10844.80803-0.808032
10934.05351-1.05351
11020.9493531.05065
11154.790290.209709
11243.115780.88422
11357.89069-2.89069
11410.3240530.675947
11554.207140.792859
11655.83749-0.83749
11733.05119-0.0511916
11843.203010.796986
11953.941641.05836
12055.80982-0.809822
12132.790410.209595
12243.132860.867142
12355.38977-0.389775
12444.27572-0.27572
12544.17381-0.173813
12643.527160.472838
12755.40124-0.401239
12843.211460.788545
12955.15536-0.155365
13044.18674-0.18674
13143.449160.550838
13244.30896-0.308957
13345.01836-1.01836
13433.2011-0.201105
13542.980441.01956
13655.40347-0.403469
13732.894770.105233
13844.62712-0.627119
13933.2067-0.206695
14045.09184-1.09184
14134.09561-1.09561
14232.046780.953221
14354.108070.891933
14454.256590.74341
14554.32710.672902
14654.331460.668537
14755.02506-0.0250577
14844.17532-0.175324
14943.467350.532651
15043.166050.833951
15153.758591.24141
15255.04284-0.0428351
15344.01064-0.0106419
15443.381340.61866
15542.836031.16397
15655.99038-0.99038
15732.919440.0805579
15843.211460.788545
15954.21340.786605
16053.621931.37807
16153.796541.20346
1625NANA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 3 & 4.08712 & -1.08712 \tabularnewline
2 & 5 & 4.43188 & 0.568117 \tabularnewline
3 & 4 & 3.89213 & 0.107872 \tabularnewline
4 & 4 & 4.08042 & -0.0804217 \tabularnewline
5 & 5 & 3.41743 & 1.58257 \tabularnewline
6 & 5 & 4.27428 & 0.725722 \tabularnewline
7 & 2 & 3.61754 & -1.61754 \tabularnewline
8 & 5 & 4.14687 & 0.853128 \tabularnewline
9 & 4 & 4.34326 & -0.343257 \tabularnewline
10 & 4 & 4.20514 & -0.20514 \tabularnewline
11 & 5 & 4.39463 & 0.605366 \tabularnewline
12 & 3 & 4.59722 & -1.59722 \tabularnewline
13 & 5 & 3.90216 & 1.09784 \tabularnewline
14 & 3 & 4.05938 & -1.05938 \tabularnewline
15 & 5 & 4.23766 & 0.762336 \tabularnewline
16 & 3 & 3.95492 & -0.954922 \tabularnewline
17 & 4 & 3.94723 & 0.0527681 \tabularnewline
18 & 5 & 4.26831 & 0.73169 \tabularnewline
19 & 4 & 4.12146 & -0.121464 \tabularnewline
20 & 3 & 4.01807 & -1.01807 \tabularnewline
21 & 4 & 4.58946 & -0.589461 \tabularnewline
22 & 4 & 3.94054 & 0.0594644 \tabularnewline
23 & 3 & 4.06126 & -1.06126 \tabularnewline
24 & 3 & 3.83976 & -0.839757 \tabularnewline
25 & 4 & 4.40273 & -0.402727 \tabularnewline
26 & 5 & 4.39207 & 0.607929 \tabularnewline
27 & 4 & 3.96683 & 0.03317 \tabularnewline
28 & 4 & 3.86813 & 0.131865 \tabularnewline
29 & 4 & 4.06528 & -0.0652752 \tabularnewline
30 & 4 & 4.02581 & -0.0258128 \tabularnewline
31 & 4 & 4.4341 & -0.434101 \tabularnewline
32 & 3 & 3.87403 & -0.874034 \tabularnewline
33 & 4 & 4.38171 & -0.381707 \tabularnewline
34 & 5 & 4.23543 & 0.764573 \tabularnewline
35 & 4 & 4.13989 & -0.139891 \tabularnewline
36 & 4 & 4.46691 & -0.466913 \tabularnewline
37 & 3 & 3.47096 & -0.470959 \tabularnewline
38 & 4 & 4.23253 & -0.232527 \tabularnewline
39 & 4 & 4.34656 & -0.346557 \tabularnewline
40 & 4 & 4.28976 & -0.289757 \tabularnewline
41 & 5 & 4.44703 & 0.552971 \tabularnewline
42 & 4 & 3.97082 & 0.0291806 \tabularnewline
43 & 3 & 4.55335 & -1.55335 \tabularnewline
44 & 3 & 3.93539 & -0.935392 \tabularnewline
45 & 4 & 4.14296 & -0.142959 \tabularnewline
46 & 4 & 3.91032 & 0.0896842 \tabularnewline
47 & 4 & 4.39787 & -0.397867 \tabularnewline
48 & 5 & 4.24911 & 0.75089 \tabularnewline
49 & 4 & 4.12205 & -0.122055 \tabularnewline
50 & 5 & 4.27317 & 0.726826 \tabularnewline
51 & 4 & 4.36236 & -0.362363 \tabularnewline
52 & 4 & 4.0498 & -0.0498015 \tabularnewline
53 & 4 & 3.67878 & 0.321216 \tabularnewline
54 & 4 & 3.80984 & 0.190155 \tabularnewline
55 & 4 & 4.16645 & -0.166454 \tabularnewline
56 & 5 & 4.25432 & 0.745675 \tabularnewline
57 & 4 & 4.15909 & -0.159091 \tabularnewline
58 & 4 & 4.39414 & -0.39414 \tabularnewline
59 & 4 & 4.19158 & -0.191576 \tabularnewline
60 & 4 & 3.86229 & 0.137713 \tabularnewline
61 & 3 & 3.48134 & -0.48134 \tabularnewline
62 & 4 & 4.05466 & -0.0546603 \tabularnewline
63 & 5 & 4.00404 & 0.995962 \tabularnewline
64 & 1 & 3.78772 & -2.78772 \tabularnewline
65 & 3 & 4.02364 & -1.02364 \tabularnewline
66 & 5 & 4.10257 & 0.897426 \tabularnewline
67 & 4 & 3.70872 & 0.291282 \tabularnewline
68 & 4 & 4.05244 & -0.0524444 \tabularnewline
69 & 3 & 4.14498 & -1.14498 \tabularnewline
70 & 4 & 4.05565 & -0.0556502 \tabularnewline
71 & 4 & 4.13838 & -0.138381 \tabularnewline
72 & 3 & 3.95999 & -0.959992 \tabularnewline
73 & 5 & 4.15749 & 0.842513 \tabularnewline
74 & 4 & 4.24762 & -0.247622 \tabularnewline
75 & 5 & 4.19754 & 0.802455 \tabularnewline
76 & 4 & 3.69254 & 0.307463 \tabularnewline
77 & 4 & 4.13578 & -0.135783 \tabularnewline
78 & 4 & 3.70839 & 0.291614 \tabularnewline
79 & 4 & 4.08261 & -0.082613 \tabularnewline
80 & 3 & 4.29653 & -1.29653 \tabularnewline
81 & 5 & 4.1631 & 0.8369 \tabularnewline
82 & NA & NA & 0.623175 \tabularnewline
83 & 5 & 5.34288 & -0.34288 \tabularnewline
84 & 4 & 3.96714 & 0.0328614 \tabularnewline
85 & 4 & 2.91328 & 1.08672 \tabularnewline
86 & 5 & 5.14093 & -0.140927 \tabularnewline
87 & 4 & 4.15572 & -0.155719 \tabularnewline
88 & 4 & 4.83162 & -0.831619 \tabularnewline
89 & 3 & 2.38642 & 0.613584 \tabularnewline
90 & 4 & 4.13536 & -0.135358 \tabularnewline
91 & 4 & 4.99038 & -0.99038 \tabularnewline
92 & 3 & 2.08441 & 0.915595 \tabularnewline
93 & 5 & 4.00331 & 0.996693 \tabularnewline
94 & 5 & 4.17053 & 0.829467 \tabularnewline
95 & 5 & 5.26982 & -0.269821 \tabularnewline
96 & 4 & 3.88331 & 0.116693 \tabularnewline
97 & 4 & 4.20415 & -0.204149 \tabularnewline
98 & 4 & 4.35913 & -0.359132 \tabularnewline
99 & 4 & 4.14614 & -0.146138 \tabularnewline
100 & 4 & 4.41855 & -0.418554 \tabularnewline
101 & 4 & 3.81504 & 0.184963 \tabularnewline
102 & 4 & 3.15793 & 0.842066 \tabularnewline
103 & 5 & 5.10451 & -0.104508 \tabularnewline
104 & 4 & 3.95186 & 0.0481447 \tabularnewline
105 & 4 & 4.66853 & -0.668525 \tabularnewline
106 & 3 & 2.33403 & 0.665967 \tabularnewline
107 & 5 & 4.85047 & 0.149534 \tabularnewline
108 & 4 & 4.80803 & -0.808032 \tabularnewline
109 & 3 & 4.05351 & -1.05351 \tabularnewline
110 & 2 & 0.949353 & 1.05065 \tabularnewline
111 & 5 & 4.79029 & 0.209709 \tabularnewline
112 & 4 & 3.11578 & 0.88422 \tabularnewline
113 & 5 & 7.89069 & -2.89069 \tabularnewline
114 & 1 & 0.324053 & 0.675947 \tabularnewline
115 & 5 & 4.20714 & 0.792859 \tabularnewline
116 & 5 & 5.83749 & -0.83749 \tabularnewline
117 & 3 & 3.05119 & -0.0511916 \tabularnewline
118 & 4 & 3.20301 & 0.796986 \tabularnewline
119 & 5 & 3.94164 & 1.05836 \tabularnewline
120 & 5 & 5.80982 & -0.809822 \tabularnewline
121 & 3 & 2.79041 & 0.209595 \tabularnewline
122 & 4 & 3.13286 & 0.867142 \tabularnewline
123 & 5 & 5.38977 & -0.389775 \tabularnewline
124 & 4 & 4.27572 & -0.27572 \tabularnewline
125 & 4 & 4.17381 & -0.173813 \tabularnewline
126 & 4 & 3.52716 & 0.472838 \tabularnewline
127 & 5 & 5.40124 & -0.401239 \tabularnewline
128 & 4 & 3.21146 & 0.788545 \tabularnewline
129 & 5 & 5.15536 & -0.155365 \tabularnewline
130 & 4 & 4.18674 & -0.18674 \tabularnewline
131 & 4 & 3.44916 & 0.550838 \tabularnewline
132 & 4 & 4.30896 & -0.308957 \tabularnewline
133 & 4 & 5.01836 & -1.01836 \tabularnewline
134 & 3 & 3.2011 & -0.201105 \tabularnewline
135 & 4 & 2.98044 & 1.01956 \tabularnewline
136 & 5 & 5.40347 & -0.403469 \tabularnewline
137 & 3 & 2.89477 & 0.105233 \tabularnewline
138 & 4 & 4.62712 & -0.627119 \tabularnewline
139 & 3 & 3.2067 & -0.206695 \tabularnewline
140 & 4 & 5.09184 & -1.09184 \tabularnewline
141 & 3 & 4.09561 & -1.09561 \tabularnewline
142 & 3 & 2.04678 & 0.953221 \tabularnewline
143 & 5 & 4.10807 & 0.891933 \tabularnewline
144 & 5 & 4.25659 & 0.74341 \tabularnewline
145 & 5 & 4.3271 & 0.672902 \tabularnewline
146 & 5 & 4.33146 & 0.668537 \tabularnewline
147 & 5 & 5.02506 & -0.0250577 \tabularnewline
148 & 4 & 4.17532 & -0.175324 \tabularnewline
149 & 4 & 3.46735 & 0.532651 \tabularnewline
150 & 4 & 3.16605 & 0.833951 \tabularnewline
151 & 5 & 3.75859 & 1.24141 \tabularnewline
152 & 5 & 5.04284 & -0.0428351 \tabularnewline
153 & 4 & 4.01064 & -0.0106419 \tabularnewline
154 & 4 & 3.38134 & 0.61866 \tabularnewline
155 & 4 & 2.83603 & 1.16397 \tabularnewline
156 & 5 & 5.99038 & -0.99038 \tabularnewline
157 & 3 & 2.91944 & 0.0805579 \tabularnewline
158 & 4 & 3.21146 & 0.788545 \tabularnewline
159 & 5 & 4.2134 & 0.786605 \tabularnewline
160 & 5 & 3.62193 & 1.37807 \tabularnewline
161 & 5 & 3.79654 & 1.20346 \tabularnewline
162 & 5 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=221546&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]3[/C][C]4.08712[/C][C]-1.08712[/C][/ROW]
[ROW][C]2[/C][C]5[/C][C]4.43188[/C][C]0.568117[/C][/ROW]
[ROW][C]3[/C][C]4[/C][C]3.89213[/C][C]0.107872[/C][/ROW]
[ROW][C]4[/C][C]4[/C][C]4.08042[/C][C]-0.0804217[/C][/ROW]
[ROW][C]5[/C][C]5[/C][C]3.41743[/C][C]1.58257[/C][/ROW]
[ROW][C]6[/C][C]5[/C][C]4.27428[/C][C]0.725722[/C][/ROW]
[ROW][C]7[/C][C]2[/C][C]3.61754[/C][C]-1.61754[/C][/ROW]
[ROW][C]8[/C][C]5[/C][C]4.14687[/C][C]0.853128[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]4.34326[/C][C]-0.343257[/C][/ROW]
[ROW][C]10[/C][C]4[/C][C]4.20514[/C][C]-0.20514[/C][/ROW]
[ROW][C]11[/C][C]5[/C][C]4.39463[/C][C]0.605366[/C][/ROW]
[ROW][C]12[/C][C]3[/C][C]4.59722[/C][C]-1.59722[/C][/ROW]
[ROW][C]13[/C][C]5[/C][C]3.90216[/C][C]1.09784[/C][/ROW]
[ROW][C]14[/C][C]3[/C][C]4.05938[/C][C]-1.05938[/C][/ROW]
[ROW][C]15[/C][C]5[/C][C]4.23766[/C][C]0.762336[/C][/ROW]
[ROW][C]16[/C][C]3[/C][C]3.95492[/C][C]-0.954922[/C][/ROW]
[ROW][C]17[/C][C]4[/C][C]3.94723[/C][C]0.0527681[/C][/ROW]
[ROW][C]18[/C][C]5[/C][C]4.26831[/C][C]0.73169[/C][/ROW]
[ROW][C]19[/C][C]4[/C][C]4.12146[/C][C]-0.121464[/C][/ROW]
[ROW][C]20[/C][C]3[/C][C]4.01807[/C][C]-1.01807[/C][/ROW]
[ROW][C]21[/C][C]4[/C][C]4.58946[/C][C]-0.589461[/C][/ROW]
[ROW][C]22[/C][C]4[/C][C]3.94054[/C][C]0.0594644[/C][/ROW]
[ROW][C]23[/C][C]3[/C][C]4.06126[/C][C]-1.06126[/C][/ROW]
[ROW][C]24[/C][C]3[/C][C]3.83976[/C][C]-0.839757[/C][/ROW]
[ROW][C]25[/C][C]4[/C][C]4.40273[/C][C]-0.402727[/C][/ROW]
[ROW][C]26[/C][C]5[/C][C]4.39207[/C][C]0.607929[/C][/ROW]
[ROW][C]27[/C][C]4[/C][C]3.96683[/C][C]0.03317[/C][/ROW]
[ROW][C]28[/C][C]4[/C][C]3.86813[/C][C]0.131865[/C][/ROW]
[ROW][C]29[/C][C]4[/C][C]4.06528[/C][C]-0.0652752[/C][/ROW]
[ROW][C]30[/C][C]4[/C][C]4.02581[/C][C]-0.0258128[/C][/ROW]
[ROW][C]31[/C][C]4[/C][C]4.4341[/C][C]-0.434101[/C][/ROW]
[ROW][C]32[/C][C]3[/C][C]3.87403[/C][C]-0.874034[/C][/ROW]
[ROW][C]33[/C][C]4[/C][C]4.38171[/C][C]-0.381707[/C][/ROW]
[ROW][C]34[/C][C]5[/C][C]4.23543[/C][C]0.764573[/C][/ROW]
[ROW][C]35[/C][C]4[/C][C]4.13989[/C][C]-0.139891[/C][/ROW]
[ROW][C]36[/C][C]4[/C][C]4.46691[/C][C]-0.466913[/C][/ROW]
[ROW][C]37[/C][C]3[/C][C]3.47096[/C][C]-0.470959[/C][/ROW]
[ROW][C]38[/C][C]4[/C][C]4.23253[/C][C]-0.232527[/C][/ROW]
[ROW][C]39[/C][C]4[/C][C]4.34656[/C][C]-0.346557[/C][/ROW]
[ROW][C]40[/C][C]4[/C][C]4.28976[/C][C]-0.289757[/C][/ROW]
[ROW][C]41[/C][C]5[/C][C]4.44703[/C][C]0.552971[/C][/ROW]
[ROW][C]42[/C][C]4[/C][C]3.97082[/C][C]0.0291806[/C][/ROW]
[ROW][C]43[/C][C]3[/C][C]4.55335[/C][C]-1.55335[/C][/ROW]
[ROW][C]44[/C][C]3[/C][C]3.93539[/C][C]-0.935392[/C][/ROW]
[ROW][C]45[/C][C]4[/C][C]4.14296[/C][C]-0.142959[/C][/ROW]
[ROW][C]46[/C][C]4[/C][C]3.91032[/C][C]0.0896842[/C][/ROW]
[ROW][C]47[/C][C]4[/C][C]4.39787[/C][C]-0.397867[/C][/ROW]
[ROW][C]48[/C][C]5[/C][C]4.24911[/C][C]0.75089[/C][/ROW]
[ROW][C]49[/C][C]4[/C][C]4.12205[/C][C]-0.122055[/C][/ROW]
[ROW][C]50[/C][C]5[/C][C]4.27317[/C][C]0.726826[/C][/ROW]
[ROW][C]51[/C][C]4[/C][C]4.36236[/C][C]-0.362363[/C][/ROW]
[ROW][C]52[/C][C]4[/C][C]4.0498[/C][C]-0.0498015[/C][/ROW]
[ROW][C]53[/C][C]4[/C][C]3.67878[/C][C]0.321216[/C][/ROW]
[ROW][C]54[/C][C]4[/C][C]3.80984[/C][C]0.190155[/C][/ROW]
[ROW][C]55[/C][C]4[/C][C]4.16645[/C][C]-0.166454[/C][/ROW]
[ROW][C]56[/C][C]5[/C][C]4.25432[/C][C]0.745675[/C][/ROW]
[ROW][C]57[/C][C]4[/C][C]4.15909[/C][C]-0.159091[/C][/ROW]
[ROW][C]58[/C][C]4[/C][C]4.39414[/C][C]-0.39414[/C][/ROW]
[ROW][C]59[/C][C]4[/C][C]4.19158[/C][C]-0.191576[/C][/ROW]
[ROW][C]60[/C][C]4[/C][C]3.86229[/C][C]0.137713[/C][/ROW]
[ROW][C]61[/C][C]3[/C][C]3.48134[/C][C]-0.48134[/C][/ROW]
[ROW][C]62[/C][C]4[/C][C]4.05466[/C][C]-0.0546603[/C][/ROW]
[ROW][C]63[/C][C]5[/C][C]4.00404[/C][C]0.995962[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]3.78772[/C][C]-2.78772[/C][/ROW]
[ROW][C]65[/C][C]3[/C][C]4.02364[/C][C]-1.02364[/C][/ROW]
[ROW][C]66[/C][C]5[/C][C]4.10257[/C][C]0.897426[/C][/ROW]
[ROW][C]67[/C][C]4[/C][C]3.70872[/C][C]0.291282[/C][/ROW]
[ROW][C]68[/C][C]4[/C][C]4.05244[/C][C]-0.0524444[/C][/ROW]
[ROW][C]69[/C][C]3[/C][C]4.14498[/C][C]-1.14498[/C][/ROW]
[ROW][C]70[/C][C]4[/C][C]4.05565[/C][C]-0.0556502[/C][/ROW]
[ROW][C]71[/C][C]4[/C][C]4.13838[/C][C]-0.138381[/C][/ROW]
[ROW][C]72[/C][C]3[/C][C]3.95999[/C][C]-0.959992[/C][/ROW]
[ROW][C]73[/C][C]5[/C][C]4.15749[/C][C]0.842513[/C][/ROW]
[ROW][C]74[/C][C]4[/C][C]4.24762[/C][C]-0.247622[/C][/ROW]
[ROW][C]75[/C][C]5[/C][C]4.19754[/C][C]0.802455[/C][/ROW]
[ROW][C]76[/C][C]4[/C][C]3.69254[/C][C]0.307463[/C][/ROW]
[ROW][C]77[/C][C]4[/C][C]4.13578[/C][C]-0.135783[/C][/ROW]
[ROW][C]78[/C][C]4[/C][C]3.70839[/C][C]0.291614[/C][/ROW]
[ROW][C]79[/C][C]4[/C][C]4.08261[/C][C]-0.082613[/C][/ROW]
[ROW][C]80[/C][C]3[/C][C]4.29653[/C][C]-1.29653[/C][/ROW]
[ROW][C]81[/C][C]5[/C][C]4.1631[/C][C]0.8369[/C][/ROW]
[ROW][C]82[/C][C]NA[/C][C]NA[/C][C]0.623175[/C][/ROW]
[ROW][C]83[/C][C]5[/C][C]5.34288[/C][C]-0.34288[/C][/ROW]
[ROW][C]84[/C][C]4[/C][C]3.96714[/C][C]0.0328614[/C][/ROW]
[ROW][C]85[/C][C]4[/C][C]2.91328[/C][C]1.08672[/C][/ROW]
[ROW][C]86[/C][C]5[/C][C]5.14093[/C][C]-0.140927[/C][/ROW]
[ROW][C]87[/C][C]4[/C][C]4.15572[/C][C]-0.155719[/C][/ROW]
[ROW][C]88[/C][C]4[/C][C]4.83162[/C][C]-0.831619[/C][/ROW]
[ROW][C]89[/C][C]3[/C][C]2.38642[/C][C]0.613584[/C][/ROW]
[ROW][C]90[/C][C]4[/C][C]4.13536[/C][C]-0.135358[/C][/ROW]
[ROW][C]91[/C][C]4[/C][C]4.99038[/C][C]-0.99038[/C][/ROW]
[ROW][C]92[/C][C]3[/C][C]2.08441[/C][C]0.915595[/C][/ROW]
[ROW][C]93[/C][C]5[/C][C]4.00331[/C][C]0.996693[/C][/ROW]
[ROW][C]94[/C][C]5[/C][C]4.17053[/C][C]0.829467[/C][/ROW]
[ROW][C]95[/C][C]5[/C][C]5.26982[/C][C]-0.269821[/C][/ROW]
[ROW][C]96[/C][C]4[/C][C]3.88331[/C][C]0.116693[/C][/ROW]
[ROW][C]97[/C][C]4[/C][C]4.20415[/C][C]-0.204149[/C][/ROW]
[ROW][C]98[/C][C]4[/C][C]4.35913[/C][C]-0.359132[/C][/ROW]
[ROW][C]99[/C][C]4[/C][C]4.14614[/C][C]-0.146138[/C][/ROW]
[ROW][C]100[/C][C]4[/C][C]4.41855[/C][C]-0.418554[/C][/ROW]
[ROW][C]101[/C][C]4[/C][C]3.81504[/C][C]0.184963[/C][/ROW]
[ROW][C]102[/C][C]4[/C][C]3.15793[/C][C]0.842066[/C][/ROW]
[ROW][C]103[/C][C]5[/C][C]5.10451[/C][C]-0.104508[/C][/ROW]
[ROW][C]104[/C][C]4[/C][C]3.95186[/C][C]0.0481447[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]4.66853[/C][C]-0.668525[/C][/ROW]
[ROW][C]106[/C][C]3[/C][C]2.33403[/C][C]0.665967[/C][/ROW]
[ROW][C]107[/C][C]5[/C][C]4.85047[/C][C]0.149534[/C][/ROW]
[ROW][C]108[/C][C]4[/C][C]4.80803[/C][C]-0.808032[/C][/ROW]
[ROW][C]109[/C][C]3[/C][C]4.05351[/C][C]-1.05351[/C][/ROW]
[ROW][C]110[/C][C]2[/C][C]0.949353[/C][C]1.05065[/C][/ROW]
[ROW][C]111[/C][C]5[/C][C]4.79029[/C][C]0.209709[/C][/ROW]
[ROW][C]112[/C][C]4[/C][C]3.11578[/C][C]0.88422[/C][/ROW]
[ROW][C]113[/C][C]5[/C][C]7.89069[/C][C]-2.89069[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]0.324053[/C][C]0.675947[/C][/ROW]
[ROW][C]115[/C][C]5[/C][C]4.20714[/C][C]0.792859[/C][/ROW]
[ROW][C]116[/C][C]5[/C][C]5.83749[/C][C]-0.83749[/C][/ROW]
[ROW][C]117[/C][C]3[/C][C]3.05119[/C][C]-0.0511916[/C][/ROW]
[ROW][C]118[/C][C]4[/C][C]3.20301[/C][C]0.796986[/C][/ROW]
[ROW][C]119[/C][C]5[/C][C]3.94164[/C][C]1.05836[/C][/ROW]
[ROW][C]120[/C][C]5[/C][C]5.80982[/C][C]-0.809822[/C][/ROW]
[ROW][C]121[/C][C]3[/C][C]2.79041[/C][C]0.209595[/C][/ROW]
[ROW][C]122[/C][C]4[/C][C]3.13286[/C][C]0.867142[/C][/ROW]
[ROW][C]123[/C][C]5[/C][C]5.38977[/C][C]-0.389775[/C][/ROW]
[ROW][C]124[/C][C]4[/C][C]4.27572[/C][C]-0.27572[/C][/ROW]
[ROW][C]125[/C][C]4[/C][C]4.17381[/C][C]-0.173813[/C][/ROW]
[ROW][C]126[/C][C]4[/C][C]3.52716[/C][C]0.472838[/C][/ROW]
[ROW][C]127[/C][C]5[/C][C]5.40124[/C][C]-0.401239[/C][/ROW]
[ROW][C]128[/C][C]4[/C][C]3.21146[/C][C]0.788545[/C][/ROW]
[ROW][C]129[/C][C]5[/C][C]5.15536[/C][C]-0.155365[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]4.18674[/C][C]-0.18674[/C][/ROW]
[ROW][C]131[/C][C]4[/C][C]3.44916[/C][C]0.550838[/C][/ROW]
[ROW][C]132[/C][C]4[/C][C]4.30896[/C][C]-0.308957[/C][/ROW]
[ROW][C]133[/C][C]4[/C][C]5.01836[/C][C]-1.01836[/C][/ROW]
[ROW][C]134[/C][C]3[/C][C]3.2011[/C][C]-0.201105[/C][/ROW]
[ROW][C]135[/C][C]4[/C][C]2.98044[/C][C]1.01956[/C][/ROW]
[ROW][C]136[/C][C]5[/C][C]5.40347[/C][C]-0.403469[/C][/ROW]
[ROW][C]137[/C][C]3[/C][C]2.89477[/C][C]0.105233[/C][/ROW]
[ROW][C]138[/C][C]4[/C][C]4.62712[/C][C]-0.627119[/C][/ROW]
[ROW][C]139[/C][C]3[/C][C]3.2067[/C][C]-0.206695[/C][/ROW]
[ROW][C]140[/C][C]4[/C][C]5.09184[/C][C]-1.09184[/C][/ROW]
[ROW][C]141[/C][C]3[/C][C]4.09561[/C][C]-1.09561[/C][/ROW]
[ROW][C]142[/C][C]3[/C][C]2.04678[/C][C]0.953221[/C][/ROW]
[ROW][C]143[/C][C]5[/C][C]4.10807[/C][C]0.891933[/C][/ROW]
[ROW][C]144[/C][C]5[/C][C]4.25659[/C][C]0.74341[/C][/ROW]
[ROW][C]145[/C][C]5[/C][C]4.3271[/C][C]0.672902[/C][/ROW]
[ROW][C]146[/C][C]5[/C][C]4.33146[/C][C]0.668537[/C][/ROW]
[ROW][C]147[/C][C]5[/C][C]5.02506[/C][C]-0.0250577[/C][/ROW]
[ROW][C]148[/C][C]4[/C][C]4.17532[/C][C]-0.175324[/C][/ROW]
[ROW][C]149[/C][C]4[/C][C]3.46735[/C][C]0.532651[/C][/ROW]
[ROW][C]150[/C][C]4[/C][C]3.16605[/C][C]0.833951[/C][/ROW]
[ROW][C]151[/C][C]5[/C][C]3.75859[/C][C]1.24141[/C][/ROW]
[ROW][C]152[/C][C]5[/C][C]5.04284[/C][C]-0.0428351[/C][/ROW]
[ROW][C]153[/C][C]4[/C][C]4.01064[/C][C]-0.0106419[/C][/ROW]
[ROW][C]154[/C][C]4[/C][C]3.38134[/C][C]0.61866[/C][/ROW]
[ROW][C]155[/C][C]4[/C][C]2.83603[/C][C]1.16397[/C][/ROW]
[ROW][C]156[/C][C]5[/C][C]5.99038[/C][C]-0.99038[/C][/ROW]
[ROW][C]157[/C][C]3[/C][C]2.91944[/C][C]0.0805579[/C][/ROW]
[ROW][C]158[/C][C]4[/C][C]3.21146[/C][C]0.788545[/C][/ROW]
[ROW][C]159[/C][C]5[/C][C]4.2134[/C][C]0.786605[/C][/ROW]
[ROW][C]160[/C][C]5[/C][C]3.62193[/C][C]1.37807[/C][/ROW]
[ROW][C]161[/C][C]5[/C][C]3.79654[/C][C]1.20346[/C][/ROW]
[ROW][C]162[/C][C]5[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=221546&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=221546&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
134.08712-1.08712
254.431880.568117
343.892130.107872
444.08042-0.0804217
553.417431.58257
654.274280.725722
723.61754-1.61754
854.146870.853128
944.34326-0.343257
1044.20514-0.20514
1154.394630.605366
1234.59722-1.59722
1353.902161.09784
1434.05938-1.05938
1554.237660.762336
1633.95492-0.954922
1743.947230.0527681
1854.268310.73169
1944.12146-0.121464
2034.01807-1.01807
2144.58946-0.589461
2243.940540.0594644
2334.06126-1.06126
2433.83976-0.839757
2544.40273-0.402727
2654.392070.607929
2743.966830.03317
2843.868130.131865
2944.06528-0.0652752
3044.02581-0.0258128
3144.4341-0.434101
3233.87403-0.874034
3344.38171-0.381707
3454.235430.764573
3544.13989-0.139891
3644.46691-0.466913
3733.47096-0.470959
3844.23253-0.232527
3944.34656-0.346557
4044.28976-0.289757
4154.447030.552971
4243.970820.0291806
4334.55335-1.55335
4433.93539-0.935392
4544.14296-0.142959
4643.910320.0896842
4744.39787-0.397867
4854.249110.75089
4944.12205-0.122055
5054.273170.726826
5144.36236-0.362363
5244.0498-0.0498015
5343.678780.321216
5443.809840.190155
5544.16645-0.166454
5654.254320.745675
5744.15909-0.159091
5844.39414-0.39414
5944.19158-0.191576
6043.862290.137713
6133.48134-0.48134
6244.05466-0.0546603
6354.004040.995962
6413.78772-2.78772
6534.02364-1.02364
6654.102570.897426
6743.708720.291282
6844.05244-0.0524444
6934.14498-1.14498
7044.05565-0.0556502
7144.13838-0.138381
7233.95999-0.959992
7354.157490.842513
7444.24762-0.247622
7554.197540.802455
7643.692540.307463
7744.13578-0.135783
7843.708390.291614
7944.08261-0.082613
8034.29653-1.29653
8154.16310.8369
82NANA0.623175
8355.34288-0.34288
8443.967140.0328614
8542.913281.08672
8655.14093-0.140927
8744.15572-0.155719
8844.83162-0.831619
8932.386420.613584
9044.13536-0.135358
9144.99038-0.99038
9232.084410.915595
9354.003310.996693
9454.170530.829467
9555.26982-0.269821
9643.883310.116693
9744.20415-0.204149
9844.35913-0.359132
9944.14614-0.146138
10044.41855-0.418554
10143.815040.184963
10243.157930.842066
10355.10451-0.104508
10443.951860.0481447
10544.66853-0.668525
10632.334030.665967
10754.850470.149534
10844.80803-0.808032
10934.05351-1.05351
11020.9493531.05065
11154.790290.209709
11243.115780.88422
11357.89069-2.89069
11410.3240530.675947
11554.207140.792859
11655.83749-0.83749
11733.05119-0.0511916
11843.203010.796986
11953.941641.05836
12055.80982-0.809822
12132.790410.209595
12243.132860.867142
12355.38977-0.389775
12444.27572-0.27572
12544.17381-0.173813
12643.527160.472838
12755.40124-0.401239
12843.211460.788545
12955.15536-0.155365
13044.18674-0.18674
13143.449160.550838
13244.30896-0.308957
13345.01836-1.01836
13433.2011-0.201105
13542.980441.01956
13655.40347-0.403469
13732.894770.105233
13844.62712-0.627119
13933.2067-0.206695
14045.09184-1.09184
14134.09561-1.09561
14232.046780.953221
14354.108070.891933
14454.256590.74341
14554.32710.672902
14654.331460.668537
14755.02506-0.0250577
14844.17532-0.175324
14943.467350.532651
15043.166050.833951
15153.758591.24141
15255.04284-0.0428351
15344.01064-0.0106419
15443.381340.61866
15542.836031.16397
15655.99038-0.99038
15732.919440.0805579
15843.211460.788545
15954.21340.786605
16053.621931.37807
16153.796541.20346
1625NANA






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.1992910.3985810.800709
100.1094310.2188620.890569
110.08362450.1672490.916376
120.9149580.1700840.0850421
130.9752360.0495290.0247645
140.9700730.05985390.0299269
150.9535310.09293840.0464692
160.9798650.04026920.0201346
170.9705970.05880520.0294026
180.9694350.06112970.0305648
190.9597560.08048870.0402443
200.9778660.04426720.0221336
210.970440.05912010.0295601
220.9565180.08696320.0434816
230.9641830.07163370.0358169
240.9625470.07490620.0374531
250.9480180.1039640.0519818
260.9509870.09802640.0490132
270.9327580.1344830.0672415
280.9109670.1780660.0890328
290.8830520.2338960.116948
300.8498490.3003020.150151
310.8231930.3536140.176807
320.8269050.3461890.173095
330.7915540.4168920.208446
340.8002930.3994140.199707
350.7578440.4843120.242156
360.7218110.5563780.278189
370.6840940.6318120.315906
380.6341010.7317980.365899
390.5852020.8295970.414798
400.534370.9312610.46563
410.5195160.9609670.480484
420.4668130.9336250.533187
430.5990360.8019290.400964
440.6104240.7791530.389576
450.5607770.8784450.439223
460.5095480.9809040.490452
470.467370.9347410.53263
480.4813470.9626930.518653
490.4322690.8645380.567731
500.4433650.886730.556635
510.4012670.8025350.598733
520.3548770.7097550.645123
530.3207290.6414570.679271
540.2794030.5588050.720597
550.2401050.4802110.759895
560.2497870.4995730.750213
570.2135780.4271560.786422
580.1859270.3718530.814073
590.156430.3128610.84357
600.1299760.2599530.870024
610.1136420.2272840.886358
620.09192440.1838490.908076
630.1081770.2163540.891823
640.5647720.8704560.435228
650.5896430.8207150.410357
660.6077950.7844110.392205
670.5694430.8611150.430557
680.5262020.9475960.473798
690.5873610.8252790.412639
700.5428180.9143640.457182
710.4977130.9954260.502287
720.5233430.9533130.476657
730.5371480.9257040.462852
740.4953040.9906080.504696
750.5081310.9837390.491869
760.4690680.9381360.530932
770.4307490.8614990.569251
780.3958610.7917210.604139
790.3571420.7142850.642858
800.4360840.8721680.563916
810.4520330.9040670.547967
820.4379990.8759980.562001
830.4058990.8117980.594101
840.3672350.734470.632765
850.4173130.8346270.582687
860.3751880.7503750.624812
870.3343430.6686860.665657
880.3443450.6886890.655655
890.3357220.6714450.664278
900.3036750.607350.696325
910.3363080.6726150.663692
920.3512220.7024450.648778
930.3778510.7557020.622149
940.3872170.7744340.612783
950.3536680.7073350.646332
960.3116110.6232210.688389
970.2754010.5508020.724599
980.256120.5122390.74388
990.2221770.4443540.777823
1000.209040.4180790.79096
1010.177850.3557010.82215
1020.1774910.3549820.822509
1030.1480790.2961570.851921
1040.1224090.2448170.877591
1050.1153160.2306320.884684
1060.1048210.2096420.895179
1070.08564560.1712910.914354
1080.0854540.1709080.914546
1090.1275440.2550880.872456
1100.1466050.293210.853395
1110.1221530.2443070.877847
1120.1476820.2953640.852318
1130.825270.349460.17473
1140.8021330.3957340.197867
1150.7875570.4248860.212443
1160.8203730.3592540.179627
1170.7837630.4324750.216237
1180.8110670.3778650.188933
1190.8095560.3808880.190444
1200.8308690.3382620.169131
1210.7982280.4035430.201772
1220.7920730.4158540.207927
1230.7555520.4888960.244448
1240.7154250.5691490.284575
1250.6645540.6708920.335446
1260.6219870.7560270.378013
1270.6523680.6952640.347632
1280.6139950.772010.386005
1290.5788140.8423720.421186
1300.5177290.9645420.482271
1310.4915780.9831560.508422
1320.4894860.9789730.510514
1330.5732120.8535770.426788
1340.5727430.8545140.427257
1350.5575360.8849280.442464
1360.6537960.6924080.346204
1370.5990540.8018920.400946
1380.613730.772540.38627
1390.5650820.8698360.434918
1400.6891030.6217930.310897
1410.8457850.308430.154215
1420.7975470.4049060.202453
1430.7797150.440570.220285
1440.7150680.5698640.284932
1450.6370150.7259690.362985
1460.5547350.890530.445265
1470.4527840.9055690.547216
1480.3487830.6975670.651217
1490.2572810.5145610.742719
1500.1893210.3786430.810679
1510.3733720.7467450.626628
1520.2743290.5486580.725671
1530.1824040.3648090.817596

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.199291 & 0.398581 & 0.800709 \tabularnewline
10 & 0.109431 & 0.218862 & 0.890569 \tabularnewline
11 & 0.0836245 & 0.167249 & 0.916376 \tabularnewline
12 & 0.914958 & 0.170084 & 0.0850421 \tabularnewline
13 & 0.975236 & 0.049529 & 0.0247645 \tabularnewline
14 & 0.970073 & 0.0598539 & 0.0299269 \tabularnewline
15 & 0.953531 & 0.0929384 & 0.0464692 \tabularnewline
16 & 0.979865 & 0.0402692 & 0.0201346 \tabularnewline
17 & 0.970597 & 0.0588052 & 0.0294026 \tabularnewline
18 & 0.969435 & 0.0611297 & 0.0305648 \tabularnewline
19 & 0.959756 & 0.0804887 & 0.0402443 \tabularnewline
20 & 0.977866 & 0.0442672 & 0.0221336 \tabularnewline
21 & 0.97044 & 0.0591201 & 0.0295601 \tabularnewline
22 & 0.956518 & 0.0869632 & 0.0434816 \tabularnewline
23 & 0.964183 & 0.0716337 & 0.0358169 \tabularnewline
24 & 0.962547 & 0.0749062 & 0.0374531 \tabularnewline
25 & 0.948018 & 0.103964 & 0.0519818 \tabularnewline
26 & 0.950987 & 0.0980264 & 0.0490132 \tabularnewline
27 & 0.932758 & 0.134483 & 0.0672415 \tabularnewline
28 & 0.910967 & 0.178066 & 0.0890328 \tabularnewline
29 & 0.883052 & 0.233896 & 0.116948 \tabularnewline
30 & 0.849849 & 0.300302 & 0.150151 \tabularnewline
31 & 0.823193 & 0.353614 & 0.176807 \tabularnewline
32 & 0.826905 & 0.346189 & 0.173095 \tabularnewline
33 & 0.791554 & 0.416892 & 0.208446 \tabularnewline
34 & 0.800293 & 0.399414 & 0.199707 \tabularnewline
35 & 0.757844 & 0.484312 & 0.242156 \tabularnewline
36 & 0.721811 & 0.556378 & 0.278189 \tabularnewline
37 & 0.684094 & 0.631812 & 0.315906 \tabularnewline
38 & 0.634101 & 0.731798 & 0.365899 \tabularnewline
39 & 0.585202 & 0.829597 & 0.414798 \tabularnewline
40 & 0.53437 & 0.931261 & 0.46563 \tabularnewline
41 & 0.519516 & 0.960967 & 0.480484 \tabularnewline
42 & 0.466813 & 0.933625 & 0.533187 \tabularnewline
43 & 0.599036 & 0.801929 & 0.400964 \tabularnewline
44 & 0.610424 & 0.779153 & 0.389576 \tabularnewline
45 & 0.560777 & 0.878445 & 0.439223 \tabularnewline
46 & 0.509548 & 0.980904 & 0.490452 \tabularnewline
47 & 0.46737 & 0.934741 & 0.53263 \tabularnewline
48 & 0.481347 & 0.962693 & 0.518653 \tabularnewline
49 & 0.432269 & 0.864538 & 0.567731 \tabularnewline
50 & 0.443365 & 0.88673 & 0.556635 \tabularnewline
51 & 0.401267 & 0.802535 & 0.598733 \tabularnewline
52 & 0.354877 & 0.709755 & 0.645123 \tabularnewline
53 & 0.320729 & 0.641457 & 0.679271 \tabularnewline
54 & 0.279403 & 0.558805 & 0.720597 \tabularnewline
55 & 0.240105 & 0.480211 & 0.759895 \tabularnewline
56 & 0.249787 & 0.499573 & 0.750213 \tabularnewline
57 & 0.213578 & 0.427156 & 0.786422 \tabularnewline
58 & 0.185927 & 0.371853 & 0.814073 \tabularnewline
59 & 0.15643 & 0.312861 & 0.84357 \tabularnewline
60 & 0.129976 & 0.259953 & 0.870024 \tabularnewline
61 & 0.113642 & 0.227284 & 0.886358 \tabularnewline
62 & 0.0919244 & 0.183849 & 0.908076 \tabularnewline
63 & 0.108177 & 0.216354 & 0.891823 \tabularnewline
64 & 0.564772 & 0.870456 & 0.435228 \tabularnewline
65 & 0.589643 & 0.820715 & 0.410357 \tabularnewline
66 & 0.607795 & 0.784411 & 0.392205 \tabularnewline
67 & 0.569443 & 0.861115 & 0.430557 \tabularnewline
68 & 0.526202 & 0.947596 & 0.473798 \tabularnewline
69 & 0.587361 & 0.825279 & 0.412639 \tabularnewline
70 & 0.542818 & 0.914364 & 0.457182 \tabularnewline
71 & 0.497713 & 0.995426 & 0.502287 \tabularnewline
72 & 0.523343 & 0.953313 & 0.476657 \tabularnewline
73 & 0.537148 & 0.925704 & 0.462852 \tabularnewline
74 & 0.495304 & 0.990608 & 0.504696 \tabularnewline
75 & 0.508131 & 0.983739 & 0.491869 \tabularnewline
76 & 0.469068 & 0.938136 & 0.530932 \tabularnewline
77 & 0.430749 & 0.861499 & 0.569251 \tabularnewline
78 & 0.395861 & 0.791721 & 0.604139 \tabularnewline
79 & 0.357142 & 0.714285 & 0.642858 \tabularnewline
80 & 0.436084 & 0.872168 & 0.563916 \tabularnewline
81 & 0.452033 & 0.904067 & 0.547967 \tabularnewline
82 & 0.437999 & 0.875998 & 0.562001 \tabularnewline
83 & 0.405899 & 0.811798 & 0.594101 \tabularnewline
84 & 0.367235 & 0.73447 & 0.632765 \tabularnewline
85 & 0.417313 & 0.834627 & 0.582687 \tabularnewline
86 & 0.375188 & 0.750375 & 0.624812 \tabularnewline
87 & 0.334343 & 0.668686 & 0.665657 \tabularnewline
88 & 0.344345 & 0.688689 & 0.655655 \tabularnewline
89 & 0.335722 & 0.671445 & 0.664278 \tabularnewline
90 & 0.303675 & 0.60735 & 0.696325 \tabularnewline
91 & 0.336308 & 0.672615 & 0.663692 \tabularnewline
92 & 0.351222 & 0.702445 & 0.648778 \tabularnewline
93 & 0.377851 & 0.755702 & 0.622149 \tabularnewline
94 & 0.387217 & 0.774434 & 0.612783 \tabularnewline
95 & 0.353668 & 0.707335 & 0.646332 \tabularnewline
96 & 0.311611 & 0.623221 & 0.688389 \tabularnewline
97 & 0.275401 & 0.550802 & 0.724599 \tabularnewline
98 & 0.25612 & 0.512239 & 0.74388 \tabularnewline
99 & 0.222177 & 0.444354 & 0.777823 \tabularnewline
100 & 0.20904 & 0.418079 & 0.79096 \tabularnewline
101 & 0.17785 & 0.355701 & 0.82215 \tabularnewline
102 & 0.177491 & 0.354982 & 0.822509 \tabularnewline
103 & 0.148079 & 0.296157 & 0.851921 \tabularnewline
104 & 0.122409 & 0.244817 & 0.877591 \tabularnewline
105 & 0.115316 & 0.230632 & 0.884684 \tabularnewline
106 & 0.104821 & 0.209642 & 0.895179 \tabularnewline
107 & 0.0856456 & 0.171291 & 0.914354 \tabularnewline
108 & 0.085454 & 0.170908 & 0.914546 \tabularnewline
109 & 0.127544 & 0.255088 & 0.872456 \tabularnewline
110 & 0.146605 & 0.29321 & 0.853395 \tabularnewline
111 & 0.122153 & 0.244307 & 0.877847 \tabularnewline
112 & 0.147682 & 0.295364 & 0.852318 \tabularnewline
113 & 0.82527 & 0.34946 & 0.17473 \tabularnewline
114 & 0.802133 & 0.395734 & 0.197867 \tabularnewline
115 & 0.787557 & 0.424886 & 0.212443 \tabularnewline
116 & 0.820373 & 0.359254 & 0.179627 \tabularnewline
117 & 0.783763 & 0.432475 & 0.216237 \tabularnewline
118 & 0.811067 & 0.377865 & 0.188933 \tabularnewline
119 & 0.809556 & 0.380888 & 0.190444 \tabularnewline
120 & 0.830869 & 0.338262 & 0.169131 \tabularnewline
121 & 0.798228 & 0.403543 & 0.201772 \tabularnewline
122 & 0.792073 & 0.415854 & 0.207927 \tabularnewline
123 & 0.755552 & 0.488896 & 0.244448 \tabularnewline
124 & 0.715425 & 0.569149 & 0.284575 \tabularnewline
125 & 0.664554 & 0.670892 & 0.335446 \tabularnewline
126 & 0.621987 & 0.756027 & 0.378013 \tabularnewline
127 & 0.652368 & 0.695264 & 0.347632 \tabularnewline
128 & 0.613995 & 0.77201 & 0.386005 \tabularnewline
129 & 0.578814 & 0.842372 & 0.421186 \tabularnewline
130 & 0.517729 & 0.964542 & 0.482271 \tabularnewline
131 & 0.491578 & 0.983156 & 0.508422 \tabularnewline
132 & 0.489486 & 0.978973 & 0.510514 \tabularnewline
133 & 0.573212 & 0.853577 & 0.426788 \tabularnewline
134 & 0.572743 & 0.854514 & 0.427257 \tabularnewline
135 & 0.557536 & 0.884928 & 0.442464 \tabularnewline
136 & 0.653796 & 0.692408 & 0.346204 \tabularnewline
137 & 0.599054 & 0.801892 & 0.400946 \tabularnewline
138 & 0.61373 & 0.77254 & 0.38627 \tabularnewline
139 & 0.565082 & 0.869836 & 0.434918 \tabularnewline
140 & 0.689103 & 0.621793 & 0.310897 \tabularnewline
141 & 0.845785 & 0.30843 & 0.154215 \tabularnewline
142 & 0.797547 & 0.404906 & 0.202453 \tabularnewline
143 & 0.779715 & 0.44057 & 0.220285 \tabularnewline
144 & 0.715068 & 0.569864 & 0.284932 \tabularnewline
145 & 0.637015 & 0.725969 & 0.362985 \tabularnewline
146 & 0.554735 & 0.89053 & 0.445265 \tabularnewline
147 & 0.452784 & 0.905569 & 0.547216 \tabularnewline
148 & 0.348783 & 0.697567 & 0.651217 \tabularnewline
149 & 0.257281 & 0.514561 & 0.742719 \tabularnewline
150 & 0.189321 & 0.378643 & 0.810679 \tabularnewline
151 & 0.373372 & 0.746745 & 0.626628 \tabularnewline
152 & 0.274329 & 0.548658 & 0.725671 \tabularnewline
153 & 0.182404 & 0.364809 & 0.817596 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=221546&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C]0.199291[/C][C]0.398581[/C][C]0.800709[/C][/ROW]
[ROW][C]10[/C][C]0.109431[/C][C]0.218862[/C][C]0.890569[/C][/ROW]
[ROW][C]11[/C][C]0.0836245[/C][C]0.167249[/C][C]0.916376[/C][/ROW]
[ROW][C]12[/C][C]0.914958[/C][C]0.170084[/C][C]0.0850421[/C][/ROW]
[ROW][C]13[/C][C]0.975236[/C][C]0.049529[/C][C]0.0247645[/C][/ROW]
[ROW][C]14[/C][C]0.970073[/C][C]0.0598539[/C][C]0.0299269[/C][/ROW]
[ROW][C]15[/C][C]0.953531[/C][C]0.0929384[/C][C]0.0464692[/C][/ROW]
[ROW][C]16[/C][C]0.979865[/C][C]0.0402692[/C][C]0.0201346[/C][/ROW]
[ROW][C]17[/C][C]0.970597[/C][C]0.0588052[/C][C]0.0294026[/C][/ROW]
[ROW][C]18[/C][C]0.969435[/C][C]0.0611297[/C][C]0.0305648[/C][/ROW]
[ROW][C]19[/C][C]0.959756[/C][C]0.0804887[/C][C]0.0402443[/C][/ROW]
[ROW][C]20[/C][C]0.977866[/C][C]0.0442672[/C][C]0.0221336[/C][/ROW]
[ROW][C]21[/C][C]0.97044[/C][C]0.0591201[/C][C]0.0295601[/C][/ROW]
[ROW][C]22[/C][C]0.956518[/C][C]0.0869632[/C][C]0.0434816[/C][/ROW]
[ROW][C]23[/C][C]0.964183[/C][C]0.0716337[/C][C]0.0358169[/C][/ROW]
[ROW][C]24[/C][C]0.962547[/C][C]0.0749062[/C][C]0.0374531[/C][/ROW]
[ROW][C]25[/C][C]0.948018[/C][C]0.103964[/C][C]0.0519818[/C][/ROW]
[ROW][C]26[/C][C]0.950987[/C][C]0.0980264[/C][C]0.0490132[/C][/ROW]
[ROW][C]27[/C][C]0.932758[/C][C]0.134483[/C][C]0.0672415[/C][/ROW]
[ROW][C]28[/C][C]0.910967[/C][C]0.178066[/C][C]0.0890328[/C][/ROW]
[ROW][C]29[/C][C]0.883052[/C][C]0.233896[/C][C]0.116948[/C][/ROW]
[ROW][C]30[/C][C]0.849849[/C][C]0.300302[/C][C]0.150151[/C][/ROW]
[ROW][C]31[/C][C]0.823193[/C][C]0.353614[/C][C]0.176807[/C][/ROW]
[ROW][C]32[/C][C]0.826905[/C][C]0.346189[/C][C]0.173095[/C][/ROW]
[ROW][C]33[/C][C]0.791554[/C][C]0.416892[/C][C]0.208446[/C][/ROW]
[ROW][C]34[/C][C]0.800293[/C][C]0.399414[/C][C]0.199707[/C][/ROW]
[ROW][C]35[/C][C]0.757844[/C][C]0.484312[/C][C]0.242156[/C][/ROW]
[ROW][C]36[/C][C]0.721811[/C][C]0.556378[/C][C]0.278189[/C][/ROW]
[ROW][C]37[/C][C]0.684094[/C][C]0.631812[/C][C]0.315906[/C][/ROW]
[ROW][C]38[/C][C]0.634101[/C][C]0.731798[/C][C]0.365899[/C][/ROW]
[ROW][C]39[/C][C]0.585202[/C][C]0.829597[/C][C]0.414798[/C][/ROW]
[ROW][C]40[/C][C]0.53437[/C][C]0.931261[/C][C]0.46563[/C][/ROW]
[ROW][C]41[/C][C]0.519516[/C][C]0.960967[/C][C]0.480484[/C][/ROW]
[ROW][C]42[/C][C]0.466813[/C][C]0.933625[/C][C]0.533187[/C][/ROW]
[ROW][C]43[/C][C]0.599036[/C][C]0.801929[/C][C]0.400964[/C][/ROW]
[ROW][C]44[/C][C]0.610424[/C][C]0.779153[/C][C]0.389576[/C][/ROW]
[ROW][C]45[/C][C]0.560777[/C][C]0.878445[/C][C]0.439223[/C][/ROW]
[ROW][C]46[/C][C]0.509548[/C][C]0.980904[/C][C]0.490452[/C][/ROW]
[ROW][C]47[/C][C]0.46737[/C][C]0.934741[/C][C]0.53263[/C][/ROW]
[ROW][C]48[/C][C]0.481347[/C][C]0.962693[/C][C]0.518653[/C][/ROW]
[ROW][C]49[/C][C]0.432269[/C][C]0.864538[/C][C]0.567731[/C][/ROW]
[ROW][C]50[/C][C]0.443365[/C][C]0.88673[/C][C]0.556635[/C][/ROW]
[ROW][C]51[/C][C]0.401267[/C][C]0.802535[/C][C]0.598733[/C][/ROW]
[ROW][C]52[/C][C]0.354877[/C][C]0.709755[/C][C]0.645123[/C][/ROW]
[ROW][C]53[/C][C]0.320729[/C][C]0.641457[/C][C]0.679271[/C][/ROW]
[ROW][C]54[/C][C]0.279403[/C][C]0.558805[/C][C]0.720597[/C][/ROW]
[ROW][C]55[/C][C]0.240105[/C][C]0.480211[/C][C]0.759895[/C][/ROW]
[ROW][C]56[/C][C]0.249787[/C][C]0.499573[/C][C]0.750213[/C][/ROW]
[ROW][C]57[/C][C]0.213578[/C][C]0.427156[/C][C]0.786422[/C][/ROW]
[ROW][C]58[/C][C]0.185927[/C][C]0.371853[/C][C]0.814073[/C][/ROW]
[ROW][C]59[/C][C]0.15643[/C][C]0.312861[/C][C]0.84357[/C][/ROW]
[ROW][C]60[/C][C]0.129976[/C][C]0.259953[/C][C]0.870024[/C][/ROW]
[ROW][C]61[/C][C]0.113642[/C][C]0.227284[/C][C]0.886358[/C][/ROW]
[ROW][C]62[/C][C]0.0919244[/C][C]0.183849[/C][C]0.908076[/C][/ROW]
[ROW][C]63[/C][C]0.108177[/C][C]0.216354[/C][C]0.891823[/C][/ROW]
[ROW][C]64[/C][C]0.564772[/C][C]0.870456[/C][C]0.435228[/C][/ROW]
[ROW][C]65[/C][C]0.589643[/C][C]0.820715[/C][C]0.410357[/C][/ROW]
[ROW][C]66[/C][C]0.607795[/C][C]0.784411[/C][C]0.392205[/C][/ROW]
[ROW][C]67[/C][C]0.569443[/C][C]0.861115[/C][C]0.430557[/C][/ROW]
[ROW][C]68[/C][C]0.526202[/C][C]0.947596[/C][C]0.473798[/C][/ROW]
[ROW][C]69[/C][C]0.587361[/C][C]0.825279[/C][C]0.412639[/C][/ROW]
[ROW][C]70[/C][C]0.542818[/C][C]0.914364[/C][C]0.457182[/C][/ROW]
[ROW][C]71[/C][C]0.497713[/C][C]0.995426[/C][C]0.502287[/C][/ROW]
[ROW][C]72[/C][C]0.523343[/C][C]0.953313[/C][C]0.476657[/C][/ROW]
[ROW][C]73[/C][C]0.537148[/C][C]0.925704[/C][C]0.462852[/C][/ROW]
[ROW][C]74[/C][C]0.495304[/C][C]0.990608[/C][C]0.504696[/C][/ROW]
[ROW][C]75[/C][C]0.508131[/C][C]0.983739[/C][C]0.491869[/C][/ROW]
[ROW][C]76[/C][C]0.469068[/C][C]0.938136[/C][C]0.530932[/C][/ROW]
[ROW][C]77[/C][C]0.430749[/C][C]0.861499[/C][C]0.569251[/C][/ROW]
[ROW][C]78[/C][C]0.395861[/C][C]0.791721[/C][C]0.604139[/C][/ROW]
[ROW][C]79[/C][C]0.357142[/C][C]0.714285[/C][C]0.642858[/C][/ROW]
[ROW][C]80[/C][C]0.436084[/C][C]0.872168[/C][C]0.563916[/C][/ROW]
[ROW][C]81[/C][C]0.452033[/C][C]0.904067[/C][C]0.547967[/C][/ROW]
[ROW][C]82[/C][C]0.437999[/C][C]0.875998[/C][C]0.562001[/C][/ROW]
[ROW][C]83[/C][C]0.405899[/C][C]0.811798[/C][C]0.594101[/C][/ROW]
[ROW][C]84[/C][C]0.367235[/C][C]0.73447[/C][C]0.632765[/C][/ROW]
[ROW][C]85[/C][C]0.417313[/C][C]0.834627[/C][C]0.582687[/C][/ROW]
[ROW][C]86[/C][C]0.375188[/C][C]0.750375[/C][C]0.624812[/C][/ROW]
[ROW][C]87[/C][C]0.334343[/C][C]0.668686[/C][C]0.665657[/C][/ROW]
[ROW][C]88[/C][C]0.344345[/C][C]0.688689[/C][C]0.655655[/C][/ROW]
[ROW][C]89[/C][C]0.335722[/C][C]0.671445[/C][C]0.664278[/C][/ROW]
[ROW][C]90[/C][C]0.303675[/C][C]0.60735[/C][C]0.696325[/C][/ROW]
[ROW][C]91[/C][C]0.336308[/C][C]0.672615[/C][C]0.663692[/C][/ROW]
[ROW][C]92[/C][C]0.351222[/C][C]0.702445[/C][C]0.648778[/C][/ROW]
[ROW][C]93[/C][C]0.377851[/C][C]0.755702[/C][C]0.622149[/C][/ROW]
[ROW][C]94[/C][C]0.387217[/C][C]0.774434[/C][C]0.612783[/C][/ROW]
[ROW][C]95[/C][C]0.353668[/C][C]0.707335[/C][C]0.646332[/C][/ROW]
[ROW][C]96[/C][C]0.311611[/C][C]0.623221[/C][C]0.688389[/C][/ROW]
[ROW][C]97[/C][C]0.275401[/C][C]0.550802[/C][C]0.724599[/C][/ROW]
[ROW][C]98[/C][C]0.25612[/C][C]0.512239[/C][C]0.74388[/C][/ROW]
[ROW][C]99[/C][C]0.222177[/C][C]0.444354[/C][C]0.777823[/C][/ROW]
[ROW][C]100[/C][C]0.20904[/C][C]0.418079[/C][C]0.79096[/C][/ROW]
[ROW][C]101[/C][C]0.17785[/C][C]0.355701[/C][C]0.82215[/C][/ROW]
[ROW][C]102[/C][C]0.177491[/C][C]0.354982[/C][C]0.822509[/C][/ROW]
[ROW][C]103[/C][C]0.148079[/C][C]0.296157[/C][C]0.851921[/C][/ROW]
[ROW][C]104[/C][C]0.122409[/C][C]0.244817[/C][C]0.877591[/C][/ROW]
[ROW][C]105[/C][C]0.115316[/C][C]0.230632[/C][C]0.884684[/C][/ROW]
[ROW][C]106[/C][C]0.104821[/C][C]0.209642[/C][C]0.895179[/C][/ROW]
[ROW][C]107[/C][C]0.0856456[/C][C]0.171291[/C][C]0.914354[/C][/ROW]
[ROW][C]108[/C][C]0.085454[/C][C]0.170908[/C][C]0.914546[/C][/ROW]
[ROW][C]109[/C][C]0.127544[/C][C]0.255088[/C][C]0.872456[/C][/ROW]
[ROW][C]110[/C][C]0.146605[/C][C]0.29321[/C][C]0.853395[/C][/ROW]
[ROW][C]111[/C][C]0.122153[/C][C]0.244307[/C][C]0.877847[/C][/ROW]
[ROW][C]112[/C][C]0.147682[/C][C]0.295364[/C][C]0.852318[/C][/ROW]
[ROW][C]113[/C][C]0.82527[/C][C]0.34946[/C][C]0.17473[/C][/ROW]
[ROW][C]114[/C][C]0.802133[/C][C]0.395734[/C][C]0.197867[/C][/ROW]
[ROW][C]115[/C][C]0.787557[/C][C]0.424886[/C][C]0.212443[/C][/ROW]
[ROW][C]116[/C][C]0.820373[/C][C]0.359254[/C][C]0.179627[/C][/ROW]
[ROW][C]117[/C][C]0.783763[/C][C]0.432475[/C][C]0.216237[/C][/ROW]
[ROW][C]118[/C][C]0.811067[/C][C]0.377865[/C][C]0.188933[/C][/ROW]
[ROW][C]119[/C][C]0.809556[/C][C]0.380888[/C][C]0.190444[/C][/ROW]
[ROW][C]120[/C][C]0.830869[/C][C]0.338262[/C][C]0.169131[/C][/ROW]
[ROW][C]121[/C][C]0.798228[/C][C]0.403543[/C][C]0.201772[/C][/ROW]
[ROW][C]122[/C][C]0.792073[/C][C]0.415854[/C][C]0.207927[/C][/ROW]
[ROW][C]123[/C][C]0.755552[/C][C]0.488896[/C][C]0.244448[/C][/ROW]
[ROW][C]124[/C][C]0.715425[/C][C]0.569149[/C][C]0.284575[/C][/ROW]
[ROW][C]125[/C][C]0.664554[/C][C]0.670892[/C][C]0.335446[/C][/ROW]
[ROW][C]126[/C][C]0.621987[/C][C]0.756027[/C][C]0.378013[/C][/ROW]
[ROW][C]127[/C][C]0.652368[/C][C]0.695264[/C][C]0.347632[/C][/ROW]
[ROW][C]128[/C][C]0.613995[/C][C]0.77201[/C][C]0.386005[/C][/ROW]
[ROW][C]129[/C][C]0.578814[/C][C]0.842372[/C][C]0.421186[/C][/ROW]
[ROW][C]130[/C][C]0.517729[/C][C]0.964542[/C][C]0.482271[/C][/ROW]
[ROW][C]131[/C][C]0.491578[/C][C]0.983156[/C][C]0.508422[/C][/ROW]
[ROW][C]132[/C][C]0.489486[/C][C]0.978973[/C][C]0.510514[/C][/ROW]
[ROW][C]133[/C][C]0.573212[/C][C]0.853577[/C][C]0.426788[/C][/ROW]
[ROW][C]134[/C][C]0.572743[/C][C]0.854514[/C][C]0.427257[/C][/ROW]
[ROW][C]135[/C][C]0.557536[/C][C]0.884928[/C][C]0.442464[/C][/ROW]
[ROW][C]136[/C][C]0.653796[/C][C]0.692408[/C][C]0.346204[/C][/ROW]
[ROW][C]137[/C][C]0.599054[/C][C]0.801892[/C][C]0.400946[/C][/ROW]
[ROW][C]138[/C][C]0.61373[/C][C]0.77254[/C][C]0.38627[/C][/ROW]
[ROW][C]139[/C][C]0.565082[/C][C]0.869836[/C][C]0.434918[/C][/ROW]
[ROW][C]140[/C][C]0.689103[/C][C]0.621793[/C][C]0.310897[/C][/ROW]
[ROW][C]141[/C][C]0.845785[/C][C]0.30843[/C][C]0.154215[/C][/ROW]
[ROW][C]142[/C][C]0.797547[/C][C]0.404906[/C][C]0.202453[/C][/ROW]
[ROW][C]143[/C][C]0.779715[/C][C]0.44057[/C][C]0.220285[/C][/ROW]
[ROW][C]144[/C][C]0.715068[/C][C]0.569864[/C][C]0.284932[/C][/ROW]
[ROW][C]145[/C][C]0.637015[/C][C]0.725969[/C][C]0.362985[/C][/ROW]
[ROW][C]146[/C][C]0.554735[/C][C]0.89053[/C][C]0.445265[/C][/ROW]
[ROW][C]147[/C][C]0.452784[/C][C]0.905569[/C][C]0.547216[/C][/ROW]
[ROW][C]148[/C][C]0.348783[/C][C]0.697567[/C][C]0.651217[/C][/ROW]
[ROW][C]149[/C][C]0.257281[/C][C]0.514561[/C][C]0.742719[/C][/ROW]
[ROW][C]150[/C][C]0.189321[/C][C]0.378643[/C][C]0.810679[/C][/ROW]
[ROW][C]151[/C][C]0.373372[/C][C]0.746745[/C][C]0.626628[/C][/ROW]
[ROW][C]152[/C][C]0.274329[/C][C]0.548658[/C][C]0.725671[/C][/ROW]
[ROW][C]153[/C][C]0.182404[/C][C]0.364809[/C][C]0.817596[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=221546&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=221546&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.1992910.3985810.800709
100.1094310.2188620.890569
110.08362450.1672490.916376
120.9149580.1700840.0850421
130.9752360.0495290.0247645
140.9700730.05985390.0299269
150.9535310.09293840.0464692
160.9798650.04026920.0201346
170.9705970.05880520.0294026
180.9694350.06112970.0305648
190.9597560.08048870.0402443
200.9778660.04426720.0221336
210.970440.05912010.0295601
220.9565180.08696320.0434816
230.9641830.07163370.0358169
240.9625470.07490620.0374531
250.9480180.1039640.0519818
260.9509870.09802640.0490132
270.9327580.1344830.0672415
280.9109670.1780660.0890328
290.8830520.2338960.116948
300.8498490.3003020.150151
310.8231930.3536140.176807
320.8269050.3461890.173095
330.7915540.4168920.208446
340.8002930.3994140.199707
350.7578440.4843120.242156
360.7218110.5563780.278189
370.6840940.6318120.315906
380.6341010.7317980.365899
390.5852020.8295970.414798
400.534370.9312610.46563
410.5195160.9609670.480484
420.4668130.9336250.533187
430.5990360.8019290.400964
440.6104240.7791530.389576
450.5607770.8784450.439223
460.5095480.9809040.490452
470.467370.9347410.53263
480.4813470.9626930.518653
490.4322690.8645380.567731
500.4433650.886730.556635
510.4012670.8025350.598733
520.3548770.7097550.645123
530.3207290.6414570.679271
540.2794030.5588050.720597
550.2401050.4802110.759895
560.2497870.4995730.750213
570.2135780.4271560.786422
580.1859270.3718530.814073
590.156430.3128610.84357
600.1299760.2599530.870024
610.1136420.2272840.886358
620.09192440.1838490.908076
630.1081770.2163540.891823
640.5647720.8704560.435228
650.5896430.8207150.410357
660.6077950.7844110.392205
670.5694430.8611150.430557
680.5262020.9475960.473798
690.5873610.8252790.412639
700.5428180.9143640.457182
710.4977130.9954260.502287
720.5233430.9533130.476657
730.5371480.9257040.462852
740.4953040.9906080.504696
750.5081310.9837390.491869
760.4690680.9381360.530932
770.4307490.8614990.569251
780.3958610.7917210.604139
790.3571420.7142850.642858
800.4360840.8721680.563916
810.4520330.9040670.547967
820.4379990.8759980.562001
830.4058990.8117980.594101
840.3672350.734470.632765
850.4173130.8346270.582687
860.3751880.7503750.624812
870.3343430.6686860.665657
880.3443450.6886890.655655
890.3357220.6714450.664278
900.3036750.607350.696325
910.3363080.6726150.663692
920.3512220.7024450.648778
930.3778510.7557020.622149
940.3872170.7744340.612783
950.3536680.7073350.646332
960.3116110.6232210.688389
970.2754010.5508020.724599
980.256120.5122390.74388
990.2221770.4443540.777823
1000.209040.4180790.79096
1010.177850.3557010.82215
1020.1774910.3549820.822509
1030.1480790.2961570.851921
1040.1224090.2448170.877591
1050.1153160.2306320.884684
1060.1048210.2096420.895179
1070.08564560.1712910.914354
1080.0854540.1709080.914546
1090.1275440.2550880.872456
1100.1466050.293210.853395
1110.1221530.2443070.877847
1120.1476820.2953640.852318
1130.825270.349460.17473
1140.8021330.3957340.197867
1150.7875570.4248860.212443
1160.8203730.3592540.179627
1170.7837630.4324750.216237
1180.8110670.3778650.188933
1190.8095560.3808880.190444
1200.8308690.3382620.169131
1210.7982280.4035430.201772
1220.7920730.4158540.207927
1230.7555520.4888960.244448
1240.7154250.5691490.284575
1250.6645540.6708920.335446
1260.6219870.7560270.378013
1270.6523680.6952640.347632
1280.6139950.772010.386005
1290.5788140.8423720.421186
1300.5177290.9645420.482271
1310.4915780.9831560.508422
1320.4894860.9789730.510514
1330.5732120.8535770.426788
1340.5727430.8545140.427257
1350.5575360.8849280.442464
1360.6537960.6924080.346204
1370.5990540.8018920.400946
1380.613730.772540.38627
1390.5650820.8698360.434918
1400.6891030.6217930.310897
1410.8457850.308430.154215
1420.7975470.4049060.202453
1430.7797150.440570.220285
1440.7150680.5698640.284932
1450.6370150.7259690.362985
1460.5547350.890530.445265
1470.4527840.9055690.547216
1480.3487830.6975670.651217
1490.2572810.5145610.742719
1500.1893210.3786430.810679
1510.3733720.7467450.626628
1520.2743290.5486580.725671
1530.1824040.3648090.817596






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0206897OK
10% type I error level130.0896552OK

\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 & 3 & 0.0206897 & OK \tabularnewline
10% type I error level & 13 & 0.0896552 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=221546&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]3[/C][C]0.0206897[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]13[/C][C]0.0896552[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=221546&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=221546&T=6

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0206897OK
10% type I error level130.0896552OK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 6 ; 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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), 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,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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')
}