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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, 27 Aug 2015 21:23:40 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Aug/27/t14407070476nh8vd73yhx84f9.htm/, Retrieved Thu, 16 May 2024 18:11:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=280338, Retrieved Thu, 16 May 2024 18:11:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact173

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2013-11-04 07:18:26] [0307e7a6407eb638caabc417e3a6b260]
- RMPD  [Multiple Regression] [] [2015-08-23 02:36:22] [82473208b72870f966ef7d4d2162cc96]
-    D      [Multiple Regression] [] [2015-08-27 20:23:40] [3e99441ea7f7f69c8fa4628f6be951c3] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
19	18	20	23	20	19	12.2
24	24	19	22	25	24	7.4
15	16	12	19	15	20	6.7
17	19	16	25	20	20	12.6
19	15	9	16	15	21	13.3
28	28	28	28	28	28	11.1
26	21	20	21	11	10	8.2
15	18	16	22	22	22	11.4
26	22	22	24	22	19	6.4
16	19	17	24	27	27	10.6
15	16	12	20	21	25	11.9
22	18	16	21	19	21	9.6
26	24	15	27	16	28	6.4
26	20	17	23	24	22	13.8
21	19	17	24	22	26	13.8
22	23	18	22	25	21	11.7
20	18	15	21	23	26	10.9
21	16	20	25	20	23	16.1
22	21	21	21	22	24	9.9
18	27	6	27	27	23	6.1
20	19	19	27	21	24	9
24	7	12	25	19	25	9.7
17	20	14	23	25	23	10.8
20	20	13	25	16	21	10.3
20	19	17	19	24	21	12.7
22	20	19	22	18	18	9.3
15	14	10	19	15	18	5.9
20	17	11	21	17	21	11.4
22	17	11	27	18	23	13
17	8	10	25	26	25	10.8
14	9	7	25	18	22	12.3
17	20	12	17	19	23	11.8
23	20	18	28	17	24	7.9
16	22	9	20	21	22	12.3
18	22	16	25	26	24	11.6
20	16	14	21	21	21	6.7
18	14	11	24	12	24	10.9
23	24	20	28	20	25	12.1
24	21	17	20	20	23	13.3
23	20	14	19	24	27	10.1
20	18	16	21	22	23	14.3
19	19	10	23	20	20	13.3
22	24	15	18	23	23	9.3
17	16	10	20	21	20	15.9
20	14	10	23	17	22	9.1
21	21	16	24	20	17	13
21	15	10	27	19	20	14.5
23	21	22	23	17	16	14.6
15	17	13	22	20	24	7.3
17	13	8	19	17	23	12.6
19	18	16	23	22	26	7.7
21	17	21	26	21	24	4.3
21	20	17	23	20	21	11.8
24	18	18	26	18	22	11.2
19	18	15	22	20	20	12.6
14	12	8	20	21	24	5.6
25	22	22	27	20	21	9.9
17	18	13	22	15	23	7.7
20	20	18	21	22	22	7.3
22	20	15	24	21	23	11.4
15	16	11	26	17	21	13.6
23	22	19	24	23	27	7.9
20	19	19	24	22	23	10.7
16	6	4	25	16	27	8.3
25	19	17	27	18	27	9.6
18	24	10	19	25	23	14.2
23	18	20	23	21	22	11.1
20	17	15	22	14	15	4.35
6	6	4	24	5	27	12.7
15	22	9	19	25	23	18.1
18	20	18	25	21	23	17.85
22	16	17	18	20	18	12.6
21	17	12	24	9	22	17.1
20	23	17	23	23	21	16.1
25	22	20	27	24	25	14.7
16	20	16	24	16	24	10.6
20	20	15	26	20	22	12.6
14	13	10	21	15	28	16.2
22	16	16	25	18	22	13.6
26	25	21	28	22	21	18.9
20	16	15	19	21	23	14.1
17	15	16	20	21	19	14.5
22	19	9	27	20	25	14.75
20	24	19	23	24	23	14.8
17	9	7	18	15	28	12.45
22	22	23	23	24	14	12.65
17	15	14	21	18	23	17.35
22	22	10	23	24	24	8.6
25	24	12	21	15	15	16.1
11	12	10	14	19	23	11.6
19	21	7	24	20	26	17.75
24	25	20	26	26	21	15.25
17	26	9	24	26	26	17.65
26	28	19	20	16	16	13.6
21	16	14	25	11	21	18.25
21	21	14	23	18	19	16
19	22	15	20	19	21	18.25
24	20	22	27	8	27	14.6
28	19	19	24	15	20	13.85
27	24	22	23	21	17	18.95
23	18	17	26	18	25	15.9
22	22	17	18	24	17	16.1
15	18	17	23	20	23	10.95
20	23	11	21	22	24	15.1
28	28	24	24	26	25	15.95
19	21	16	24	23	22	14.6
22	21	13	23	19	16	15.4
21	20	15	21	21	18	15.4
20	18	15	24	23	27	17.6
19	17	11	19	19	17	13.35
17	17	13	23	16	24	15.35
21	23	7	18	23	27	19.1
12	14	9	20	23	19	12.9
20	21	12	27	20	25	12.6
18	14	14	19	19	24	10.35
21	24	22	25	26	24	15.4
24	16	19	25	9	24	9.6
17	17	16	17	13	17	14.85
8	17	22	5	6	8	19.25
22	21	20	19	17	14	13.6
17	19	15	28	23	28	12.75
25	19	11	27	20	24	9.85
18	11	9	16	17	15	12.65
23	15	18	23	18	25	11.9
21	18	11	26	20	28	16.6
21	19	14	24	18	24	11.2
24	23	10	23	23	25	15.25
17	16	16	19	12	22	12.4
15	11	11	19	16	25	15.85
22	21	16	24	24	22	18.15
19	14	13	20	23	26	11.15
19	20	14	19	19	21	12.35
19	19	10	23	28	21	15.6
22	21	19	25	23	23	15.6
23	22	17	25	23	26	18.4
25	23	19	27	28	25	13.1
20	18	12	26	21	26	12.85
23	23	8	23	25	19	9.5
21	20	17	22	18	21	4.5
23	23	17	22	28	24	13.6
11	13	7	17	9	6	11.7
21	21	23	25	22	22	12.4
21	19	17	22	18	24	14.9
22	19	13	21	22	17	17.75
21	18	8	24	15	20	11.2
22	19	16	26	24	28	14.6
18	10	13	24	12	24	14.05
24	24	15	22	25	21	13.35
20	21	15	23	24	26	11.85
18	18	14	23	18	26	14.75
14	16	11	20	22	23	13.2
17	20	19	25	25	24	7.85
18	12	12	21	14	20	7.85
19	15	18	17	16	16	10.95
15	14	15	20	13	20	9.95
24	18	20	22	19	23	14.9
19	19	12	23	16	18	13.4
23	24	19	21	23	23	16.85
23	21	18	24	20	26	10.95
17	22	8	25	26	24	12.2
22	20	18	21	21	23	15.2
16	16	13	16	25	24	8.1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280338&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280338&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280338&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 time7 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
TOT [t] = + 12.2975 -0.00617363I1[t] + 0.223732I2[t] -0.0578736I3[t] -0.105579E1[t] -0.0503152E2[t] + 0.0184564E3[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT
[t] =  +  12.2975 -0.00617363I1[t] +  0.223732I2[t] -0.0578736I3[t] -0.105579E1[t] -0.0503152E2[t] +  0.0184564E3[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280338&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT
[t] =  +  12.2975 -0.00617363I1[t] +  0.223732I2[t] -0.0578736I3[t] -0.105579E1[t] -0.0503152E2[t] +  0.0184564E3[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280338&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280338&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
TOT [t] = + 12.2975 -0.00617363I1[t] + 0.223732I2[t] -0.0578736I3[t] -0.105579E1[t] -0.0503152E2[t] + 0.0184564E3[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)12.29752.259125.4432.00398e-071.00199e-07
I1-0.006173630.0997035-0.061920.9507060.475353
I20.2237320.09058672.470.01460260.00730128
I3-0.05787360.073048-0.79230.4294150.214708
E1-0.1055790.100673-1.0490.2959330.147967
E2-0.05031520.0738694-0.68110.4968010.2484
E30.01845640.08545950.2160.8292980.414649

\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) & 12.2975 & 2.25912 & 5.443 & 2.00398e-07 & 1.00199e-07 \tabularnewline
I1 & -0.00617363 & 0.0997035 & -0.06192 & 0.950706 & 0.475353 \tabularnewline
I2 & 0.223732 & 0.0905867 & 2.47 & 0.0146026 & 0.00730128 \tabularnewline
I3 & -0.0578736 & 0.073048 & -0.7923 & 0.429415 & 0.214708 \tabularnewline
E1 & -0.105579 & 0.100673 & -1.049 & 0.295933 & 0.147967 \tabularnewline
E2 & -0.0503152 & 0.0738694 & -0.6811 & 0.496801 & 0.2484 \tabularnewline
E3 & 0.0184564 & 0.0854595 & 0.216 & 0.829298 & 0.414649 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280338&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]12.2975[/C][C]2.25912[/C][C]5.443[/C][C]2.00398e-07[/C][C]1.00199e-07[/C][/ROW]
[ROW][C]I1[/C][C]-0.00617363[/C][C]0.0997035[/C][C]-0.06192[/C][C]0.950706[/C][C]0.475353[/C][/ROW]
[ROW][C]I2[/C][C]0.223732[/C][C]0.0905867[/C][C]2.47[/C][C]0.0146026[/C][C]0.00730128[/C][/ROW]
[ROW][C]I3[/C][C]-0.0578736[/C][C]0.073048[/C][C]-0.7923[/C][C]0.429415[/C][C]0.214708[/C][/ROW]
[ROW][C]E1[/C][C]-0.105579[/C][C]0.100673[/C][C]-1.049[/C][C]0.295933[/C][C]0.147967[/C][/ROW]
[ROW][C]E2[/C][C]-0.0503152[/C][C]0.0738694[/C][C]-0.6811[/C][C]0.496801[/C][C]0.2484[/C][/ROW]
[ROW][C]E3[/C][C]0.0184564[/C][C]0.0854595[/C][C]0.216[/C][C]0.829298[/C][C]0.414649[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280338&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280338&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)12.29752.259125.4432.00398e-071.00199e-07
I1-0.006173630.0997035-0.061920.9507060.475353
I20.2237320.09058672.470.01460260.00730128
I3-0.05787360.073048-0.79230.4294150.214708
E1-0.1055790.100673-1.0490.2959330.147967
E2-0.05031520.0738694-0.68110.4968010.2484
E30.01845640.08545950.2160.8292980.414649






Multiple Linear Regression - Regression Statistics
Multiple R0.224783
R-squared0.0505273
Adjusted R-squared0.0137736
F-TEST (value)1.37475
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value0.228072
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.3025
Sum Squared Residuals1690.51

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.224783 \tabularnewline
R-squared & 0.0505273 \tabularnewline
Adjusted R-squared & 0.0137736 \tabularnewline
F-TEST (value) & 1.37475 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 155 \tabularnewline
p-value & 0.228072 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.3025 \tabularnewline
Sum Squared Residuals & 1690.51 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280338&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.224783[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0505273[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0137736[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.37475[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]155[/C][/ROW]
[ROW][C]p-value[/C][C]0.228072[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.3025[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1690.51[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280338&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280338&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 R0.224783
R-squared0.0505273
Adjusted R-squared0.0137736
F-TEST (value)1.37475
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value0.228072
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.3025
Sum Squared Residuals1690.51






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.211.96590.234051
27.413.2816-5.88163
36.712.6985-5.99852
412.612.24080.359179
513.312.95890.341098
611.112.9204-1.82041
78.213.0918-4.89182
811.412.2825-0.882455
96.412.4957-6.09571
1010.612.0717-1.47169
1111.912.3833-0.483327
129.612.4773-2.87731
136.413.4995-7.09955
1413.812.39791.40207
1513.812.27391.52606
1611.713.0728-1.37275
1710.912.4385-1.53855
1816.111.36884.73119
199.912.7636-2.86356
206.114.0952-7.99525
21911.861-2.86103
229.79.88691-0.186912
2310.812.5953-1.79525
2410.312.8394-2.53937
2512.712.61510.0849052
269.312.6405-3.34052
275.912.3299-6.42989
2811.412.6559-1.25592
291311.99671.0033
3010.89.91740.8826
3112.310.68041.61957
3211.813.6464-1.84636
337.912.2198-4.3198
3412.313.8378-1.5378
3511.612.6778-1.07778
366.712.0573-5.35731
3710.911.9873-1.08728
3812.112.8665-0.766492
3913.313.17050.12954
4010.113.1047-3.00467
4114.312.37561.92438
4213.312.78690.513127
439.314.03-4.72996
4415.912.39443.50556
459.111.8499-2.7499
461312.71380.286199
4714.511.50762.9924
4814.612.59232.00772
497.312.3699-5.06989
5012.612.20120.398796
517.712.226-4.52601
524.311.3972-7.09723
5311.812.6116-0.811599
5411.211.8901-0.690091
5512.612.37940.220648
565.611.7076-6.10761
579.912.3227-2.42269
587.712.8144-5.11439
597.312.6889-5.38888
6011.412.6022-1.20219
6113.611.93521.66484
627.912.7852-4.88518
6310.712.109-1.409
648.310.3634-2.06342
659.612.1522-2.55223
6614.214.13780.0621849
6711.111.9463-0.846308
684.3512.3591-8.00906
6912.711.08421.6158
7018.113.76674.33325
7117.8512.34775.50231
7212.612.1830.416978
7317.112.69614.40388
7416.113.1382.96198
7514.712.3112.389
7610.612.8514-2.25139
7712.612.43520.16476
7816.212.08574.11427
7913.611.67631.9237
8018.912.83946.06063
8114.112.24751.8525
8214.511.8052.69499
8314.7512.49622.25381
8414.813.23261.56739
8512.4511.66260.787359
8612.6512.37520.274807
8717.3512.045.31005
888.613.3121-4.71211
8916.114.12321.9768
9011.612.326-0.72603
9117.7513.41314.33688
9215.2512.91952.33051
9317.6514.12653.52351
9413.614.6806-1.08056
9518.2512.1326.11803
961613.07272.92733
9718.2513.55424.69579
9814.612.59592.00408
9913.8512.35641.49355
10018.9513.0565.89402
10115.912.00953.89049
10216.113.30572.7943
10310.9512.2381-1.28809
10415.113.80211.29789
10515.9513.61952.33052
10614.612.66751.93252
10715.413.01872.38131
10815.412.83282.56718
10917.612.14035.45973
11013.3512.69880.651204
11115.3512.45322.89678
11219.114.34924.75078
11312.911.91660.983365
11412.612.7824-0.182383
11510.3511.9893-1.63935
11615.412.75952.64052
1179.611.9801-2.38008
11814.8512.93481.91518
11919.2514.09625.15381
12013.613.09960.500397
12112.7511.97870.771336
1229.8512.3435-2.49347
12312.6511.85880.791222
12411.911.59720.302826
12516.612.32384.27617
12611.212.6119-1.41191
12715.2513.59231.65773
12812.412.6425-0.242532
12915.8511.67974.17031
13018.1512.59865.55135
13111.1511.7711-0.62112
13212.3513.2702-0.920197
13315.612.40283.19719
13415.612.38823.21178
13518.412.77695.62311
13613.112.39130.708657
13712.8512.18490.665093
1389.513.5028-4.00282
1394.512.8178-8.31781
14013.613.02890.571126
14111.712.596-0.896041
14212.412.19480.205242
14314.912.64942.25055
14417.7512.64995.10011
14511.212.8125-1.61254
14614.612.05082.54924
14714.0510.97663.07339
14813.3513.4578-0.107757
14911.8512.8483-0.998273
15014.7512.54922.20081
15113.212.36010.839854
1527.8512.1132-4.26318
1537.8511.6242-3.77422
15410.9512.1899-1.23986
1559.9512.0725-2.12248
15614.912.16482.7352
15713.412.83550.564526
15816.8513.47563.37444
15910.9512.7518-1.80181
16012.213.1469-0.94694
16115.212.74532.45469
1628.112.5219-4.42188

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.2 & 11.9659 & 0.234051 \tabularnewline
2 & 7.4 & 13.2816 & -5.88163 \tabularnewline
3 & 6.7 & 12.6985 & -5.99852 \tabularnewline
4 & 12.6 & 12.2408 & 0.359179 \tabularnewline
5 & 13.3 & 12.9589 & 0.341098 \tabularnewline
6 & 11.1 & 12.9204 & -1.82041 \tabularnewline
7 & 8.2 & 13.0918 & -4.89182 \tabularnewline
8 & 11.4 & 12.2825 & -0.882455 \tabularnewline
9 & 6.4 & 12.4957 & -6.09571 \tabularnewline
10 & 10.6 & 12.0717 & -1.47169 \tabularnewline
11 & 11.9 & 12.3833 & -0.483327 \tabularnewline
12 & 9.6 & 12.4773 & -2.87731 \tabularnewline
13 & 6.4 & 13.4995 & -7.09955 \tabularnewline
14 & 13.8 & 12.3979 & 1.40207 \tabularnewline
15 & 13.8 & 12.2739 & 1.52606 \tabularnewline
16 & 11.7 & 13.0728 & -1.37275 \tabularnewline
17 & 10.9 & 12.4385 & -1.53855 \tabularnewline
18 & 16.1 & 11.3688 & 4.73119 \tabularnewline
19 & 9.9 & 12.7636 & -2.86356 \tabularnewline
20 & 6.1 & 14.0952 & -7.99525 \tabularnewline
21 & 9 & 11.861 & -2.86103 \tabularnewline
22 & 9.7 & 9.88691 & -0.186912 \tabularnewline
23 & 10.8 & 12.5953 & -1.79525 \tabularnewline
24 & 10.3 & 12.8394 & -2.53937 \tabularnewline
25 & 12.7 & 12.6151 & 0.0849052 \tabularnewline
26 & 9.3 & 12.6405 & -3.34052 \tabularnewline
27 & 5.9 & 12.3299 & -6.42989 \tabularnewline
28 & 11.4 & 12.6559 & -1.25592 \tabularnewline
29 & 13 & 11.9967 & 1.0033 \tabularnewline
30 & 10.8 & 9.9174 & 0.8826 \tabularnewline
31 & 12.3 & 10.6804 & 1.61957 \tabularnewline
32 & 11.8 & 13.6464 & -1.84636 \tabularnewline
33 & 7.9 & 12.2198 & -4.3198 \tabularnewline
34 & 12.3 & 13.8378 & -1.5378 \tabularnewline
35 & 11.6 & 12.6778 & -1.07778 \tabularnewline
36 & 6.7 & 12.0573 & -5.35731 \tabularnewline
37 & 10.9 & 11.9873 & -1.08728 \tabularnewline
38 & 12.1 & 12.8665 & -0.766492 \tabularnewline
39 & 13.3 & 13.1705 & 0.12954 \tabularnewline
40 & 10.1 & 13.1047 & -3.00467 \tabularnewline
41 & 14.3 & 12.3756 & 1.92438 \tabularnewline
42 & 13.3 & 12.7869 & 0.513127 \tabularnewline
43 & 9.3 & 14.03 & -4.72996 \tabularnewline
44 & 15.9 & 12.3944 & 3.50556 \tabularnewline
45 & 9.1 & 11.8499 & -2.7499 \tabularnewline
46 & 13 & 12.7138 & 0.286199 \tabularnewline
47 & 14.5 & 11.5076 & 2.9924 \tabularnewline
48 & 14.6 & 12.5923 & 2.00772 \tabularnewline
49 & 7.3 & 12.3699 & -5.06989 \tabularnewline
50 & 12.6 & 12.2012 & 0.398796 \tabularnewline
51 & 7.7 & 12.226 & -4.52601 \tabularnewline
52 & 4.3 & 11.3972 & -7.09723 \tabularnewline
53 & 11.8 & 12.6116 & -0.811599 \tabularnewline
54 & 11.2 & 11.8901 & -0.690091 \tabularnewline
55 & 12.6 & 12.3794 & 0.220648 \tabularnewline
56 & 5.6 & 11.7076 & -6.10761 \tabularnewline
57 & 9.9 & 12.3227 & -2.42269 \tabularnewline
58 & 7.7 & 12.8144 & -5.11439 \tabularnewline
59 & 7.3 & 12.6889 & -5.38888 \tabularnewline
60 & 11.4 & 12.6022 & -1.20219 \tabularnewline
61 & 13.6 & 11.9352 & 1.66484 \tabularnewline
62 & 7.9 & 12.7852 & -4.88518 \tabularnewline
63 & 10.7 & 12.109 & -1.409 \tabularnewline
64 & 8.3 & 10.3634 & -2.06342 \tabularnewline
65 & 9.6 & 12.1522 & -2.55223 \tabularnewline
66 & 14.2 & 14.1378 & 0.0621849 \tabularnewline
67 & 11.1 & 11.9463 & -0.846308 \tabularnewline
68 & 4.35 & 12.3591 & -8.00906 \tabularnewline
69 & 12.7 & 11.0842 & 1.6158 \tabularnewline
70 & 18.1 & 13.7667 & 4.33325 \tabularnewline
71 & 17.85 & 12.3477 & 5.50231 \tabularnewline
72 & 12.6 & 12.183 & 0.416978 \tabularnewline
73 & 17.1 & 12.6961 & 4.40388 \tabularnewline
74 & 16.1 & 13.138 & 2.96198 \tabularnewline
75 & 14.7 & 12.311 & 2.389 \tabularnewline
76 & 10.6 & 12.8514 & -2.25139 \tabularnewline
77 & 12.6 & 12.4352 & 0.16476 \tabularnewline
78 & 16.2 & 12.0857 & 4.11427 \tabularnewline
79 & 13.6 & 11.6763 & 1.9237 \tabularnewline
80 & 18.9 & 12.8394 & 6.06063 \tabularnewline
81 & 14.1 & 12.2475 & 1.8525 \tabularnewline
82 & 14.5 & 11.805 & 2.69499 \tabularnewline
83 & 14.75 & 12.4962 & 2.25381 \tabularnewline
84 & 14.8 & 13.2326 & 1.56739 \tabularnewline
85 & 12.45 & 11.6626 & 0.787359 \tabularnewline
86 & 12.65 & 12.3752 & 0.274807 \tabularnewline
87 & 17.35 & 12.04 & 5.31005 \tabularnewline
88 & 8.6 & 13.3121 & -4.71211 \tabularnewline
89 & 16.1 & 14.1232 & 1.9768 \tabularnewline
90 & 11.6 & 12.326 & -0.72603 \tabularnewline
91 & 17.75 & 13.4131 & 4.33688 \tabularnewline
92 & 15.25 & 12.9195 & 2.33051 \tabularnewline
93 & 17.65 & 14.1265 & 3.52351 \tabularnewline
94 & 13.6 & 14.6806 & -1.08056 \tabularnewline
95 & 18.25 & 12.132 & 6.11803 \tabularnewline
96 & 16 & 13.0727 & 2.92733 \tabularnewline
97 & 18.25 & 13.5542 & 4.69579 \tabularnewline
98 & 14.6 & 12.5959 & 2.00408 \tabularnewline
99 & 13.85 & 12.3564 & 1.49355 \tabularnewline
100 & 18.95 & 13.056 & 5.89402 \tabularnewline
101 & 15.9 & 12.0095 & 3.89049 \tabularnewline
102 & 16.1 & 13.3057 & 2.7943 \tabularnewline
103 & 10.95 & 12.2381 & -1.28809 \tabularnewline
104 & 15.1 & 13.8021 & 1.29789 \tabularnewline
105 & 15.95 & 13.6195 & 2.33052 \tabularnewline
106 & 14.6 & 12.6675 & 1.93252 \tabularnewline
107 & 15.4 & 13.0187 & 2.38131 \tabularnewline
108 & 15.4 & 12.8328 & 2.56718 \tabularnewline
109 & 17.6 & 12.1403 & 5.45973 \tabularnewline
110 & 13.35 & 12.6988 & 0.651204 \tabularnewline
111 & 15.35 & 12.4532 & 2.89678 \tabularnewline
112 & 19.1 & 14.3492 & 4.75078 \tabularnewline
113 & 12.9 & 11.9166 & 0.983365 \tabularnewline
114 & 12.6 & 12.7824 & -0.182383 \tabularnewline
115 & 10.35 & 11.9893 & -1.63935 \tabularnewline
116 & 15.4 & 12.7595 & 2.64052 \tabularnewline
117 & 9.6 & 11.9801 & -2.38008 \tabularnewline
118 & 14.85 & 12.9348 & 1.91518 \tabularnewline
119 & 19.25 & 14.0962 & 5.15381 \tabularnewline
120 & 13.6 & 13.0996 & 0.500397 \tabularnewline
121 & 12.75 & 11.9787 & 0.771336 \tabularnewline
122 & 9.85 & 12.3435 & -2.49347 \tabularnewline
123 & 12.65 & 11.8588 & 0.791222 \tabularnewline
124 & 11.9 & 11.5972 & 0.302826 \tabularnewline
125 & 16.6 & 12.3238 & 4.27617 \tabularnewline
126 & 11.2 & 12.6119 & -1.41191 \tabularnewline
127 & 15.25 & 13.5923 & 1.65773 \tabularnewline
128 & 12.4 & 12.6425 & -0.242532 \tabularnewline
129 & 15.85 & 11.6797 & 4.17031 \tabularnewline
130 & 18.15 & 12.5986 & 5.55135 \tabularnewline
131 & 11.15 & 11.7711 & -0.62112 \tabularnewline
132 & 12.35 & 13.2702 & -0.920197 \tabularnewline
133 & 15.6 & 12.4028 & 3.19719 \tabularnewline
134 & 15.6 & 12.3882 & 3.21178 \tabularnewline
135 & 18.4 & 12.7769 & 5.62311 \tabularnewline
136 & 13.1 & 12.3913 & 0.708657 \tabularnewline
137 & 12.85 & 12.1849 & 0.665093 \tabularnewline
138 & 9.5 & 13.5028 & -4.00282 \tabularnewline
139 & 4.5 & 12.8178 & -8.31781 \tabularnewline
140 & 13.6 & 13.0289 & 0.571126 \tabularnewline
141 & 11.7 & 12.596 & -0.896041 \tabularnewline
142 & 12.4 & 12.1948 & 0.205242 \tabularnewline
143 & 14.9 & 12.6494 & 2.25055 \tabularnewline
144 & 17.75 & 12.6499 & 5.10011 \tabularnewline
145 & 11.2 & 12.8125 & -1.61254 \tabularnewline
146 & 14.6 & 12.0508 & 2.54924 \tabularnewline
147 & 14.05 & 10.9766 & 3.07339 \tabularnewline
148 & 13.35 & 13.4578 & -0.107757 \tabularnewline
149 & 11.85 & 12.8483 & -0.998273 \tabularnewline
150 & 14.75 & 12.5492 & 2.20081 \tabularnewline
151 & 13.2 & 12.3601 & 0.839854 \tabularnewline
152 & 7.85 & 12.1132 & -4.26318 \tabularnewline
153 & 7.85 & 11.6242 & -3.77422 \tabularnewline
154 & 10.95 & 12.1899 & -1.23986 \tabularnewline
155 & 9.95 & 12.0725 & -2.12248 \tabularnewline
156 & 14.9 & 12.1648 & 2.7352 \tabularnewline
157 & 13.4 & 12.8355 & 0.564526 \tabularnewline
158 & 16.85 & 13.4756 & 3.37444 \tabularnewline
159 & 10.95 & 12.7518 & -1.80181 \tabularnewline
160 & 12.2 & 13.1469 & -0.94694 \tabularnewline
161 & 15.2 & 12.7453 & 2.45469 \tabularnewline
162 & 8.1 & 12.5219 & -4.42188 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280338&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]12.2[/C][C]11.9659[/C][C]0.234051[/C][/ROW]
[ROW][C]2[/C][C]7.4[/C][C]13.2816[/C][C]-5.88163[/C][/ROW]
[ROW][C]3[/C][C]6.7[/C][C]12.6985[/C][C]-5.99852[/C][/ROW]
[ROW][C]4[/C][C]12.6[/C][C]12.2408[/C][C]0.359179[/C][/ROW]
[ROW][C]5[/C][C]13.3[/C][C]12.9589[/C][C]0.341098[/C][/ROW]
[ROW][C]6[/C][C]11.1[/C][C]12.9204[/C][C]-1.82041[/C][/ROW]
[ROW][C]7[/C][C]8.2[/C][C]13.0918[/C][C]-4.89182[/C][/ROW]
[ROW][C]8[/C][C]11.4[/C][C]12.2825[/C][C]-0.882455[/C][/ROW]
[ROW][C]9[/C][C]6.4[/C][C]12.4957[/C][C]-6.09571[/C][/ROW]
[ROW][C]10[/C][C]10.6[/C][C]12.0717[/C][C]-1.47169[/C][/ROW]
[ROW][C]11[/C][C]11.9[/C][C]12.3833[/C][C]-0.483327[/C][/ROW]
[ROW][C]12[/C][C]9.6[/C][C]12.4773[/C][C]-2.87731[/C][/ROW]
[ROW][C]13[/C][C]6.4[/C][C]13.4995[/C][C]-7.09955[/C][/ROW]
[ROW][C]14[/C][C]13.8[/C][C]12.3979[/C][C]1.40207[/C][/ROW]
[ROW][C]15[/C][C]13.8[/C][C]12.2739[/C][C]1.52606[/C][/ROW]
[ROW][C]16[/C][C]11.7[/C][C]13.0728[/C][C]-1.37275[/C][/ROW]
[ROW][C]17[/C][C]10.9[/C][C]12.4385[/C][C]-1.53855[/C][/ROW]
[ROW][C]18[/C][C]16.1[/C][C]11.3688[/C][C]4.73119[/C][/ROW]
[ROW][C]19[/C][C]9.9[/C][C]12.7636[/C][C]-2.86356[/C][/ROW]
[ROW][C]20[/C][C]6.1[/C][C]14.0952[/C][C]-7.99525[/C][/ROW]
[ROW][C]21[/C][C]9[/C][C]11.861[/C][C]-2.86103[/C][/ROW]
[ROW][C]22[/C][C]9.7[/C][C]9.88691[/C][C]-0.186912[/C][/ROW]
[ROW][C]23[/C][C]10.8[/C][C]12.5953[/C][C]-1.79525[/C][/ROW]
[ROW][C]24[/C][C]10.3[/C][C]12.8394[/C][C]-2.53937[/C][/ROW]
[ROW][C]25[/C][C]12.7[/C][C]12.6151[/C][C]0.0849052[/C][/ROW]
[ROW][C]26[/C][C]9.3[/C][C]12.6405[/C][C]-3.34052[/C][/ROW]
[ROW][C]27[/C][C]5.9[/C][C]12.3299[/C][C]-6.42989[/C][/ROW]
[ROW][C]28[/C][C]11.4[/C][C]12.6559[/C][C]-1.25592[/C][/ROW]
[ROW][C]29[/C][C]13[/C][C]11.9967[/C][C]1.0033[/C][/ROW]
[ROW][C]30[/C][C]10.8[/C][C]9.9174[/C][C]0.8826[/C][/ROW]
[ROW][C]31[/C][C]12.3[/C][C]10.6804[/C][C]1.61957[/C][/ROW]
[ROW][C]32[/C][C]11.8[/C][C]13.6464[/C][C]-1.84636[/C][/ROW]
[ROW][C]33[/C][C]7.9[/C][C]12.2198[/C][C]-4.3198[/C][/ROW]
[ROW][C]34[/C][C]12.3[/C][C]13.8378[/C][C]-1.5378[/C][/ROW]
[ROW][C]35[/C][C]11.6[/C][C]12.6778[/C][C]-1.07778[/C][/ROW]
[ROW][C]36[/C][C]6.7[/C][C]12.0573[/C][C]-5.35731[/C][/ROW]
[ROW][C]37[/C][C]10.9[/C][C]11.9873[/C][C]-1.08728[/C][/ROW]
[ROW][C]38[/C][C]12.1[/C][C]12.8665[/C][C]-0.766492[/C][/ROW]
[ROW][C]39[/C][C]13.3[/C][C]13.1705[/C][C]0.12954[/C][/ROW]
[ROW][C]40[/C][C]10.1[/C][C]13.1047[/C][C]-3.00467[/C][/ROW]
[ROW][C]41[/C][C]14.3[/C][C]12.3756[/C][C]1.92438[/C][/ROW]
[ROW][C]42[/C][C]13.3[/C][C]12.7869[/C][C]0.513127[/C][/ROW]
[ROW][C]43[/C][C]9.3[/C][C]14.03[/C][C]-4.72996[/C][/ROW]
[ROW][C]44[/C][C]15.9[/C][C]12.3944[/C][C]3.50556[/C][/ROW]
[ROW][C]45[/C][C]9.1[/C][C]11.8499[/C][C]-2.7499[/C][/ROW]
[ROW][C]46[/C][C]13[/C][C]12.7138[/C][C]0.286199[/C][/ROW]
[ROW][C]47[/C][C]14.5[/C][C]11.5076[/C][C]2.9924[/C][/ROW]
[ROW][C]48[/C][C]14.6[/C][C]12.5923[/C][C]2.00772[/C][/ROW]
[ROW][C]49[/C][C]7.3[/C][C]12.3699[/C][C]-5.06989[/C][/ROW]
[ROW][C]50[/C][C]12.6[/C][C]12.2012[/C][C]0.398796[/C][/ROW]
[ROW][C]51[/C][C]7.7[/C][C]12.226[/C][C]-4.52601[/C][/ROW]
[ROW][C]52[/C][C]4.3[/C][C]11.3972[/C][C]-7.09723[/C][/ROW]
[ROW][C]53[/C][C]11.8[/C][C]12.6116[/C][C]-0.811599[/C][/ROW]
[ROW][C]54[/C][C]11.2[/C][C]11.8901[/C][C]-0.690091[/C][/ROW]
[ROW][C]55[/C][C]12.6[/C][C]12.3794[/C][C]0.220648[/C][/ROW]
[ROW][C]56[/C][C]5.6[/C][C]11.7076[/C][C]-6.10761[/C][/ROW]
[ROW][C]57[/C][C]9.9[/C][C]12.3227[/C][C]-2.42269[/C][/ROW]
[ROW][C]58[/C][C]7.7[/C][C]12.8144[/C][C]-5.11439[/C][/ROW]
[ROW][C]59[/C][C]7.3[/C][C]12.6889[/C][C]-5.38888[/C][/ROW]
[ROW][C]60[/C][C]11.4[/C][C]12.6022[/C][C]-1.20219[/C][/ROW]
[ROW][C]61[/C][C]13.6[/C][C]11.9352[/C][C]1.66484[/C][/ROW]
[ROW][C]62[/C][C]7.9[/C][C]12.7852[/C][C]-4.88518[/C][/ROW]
[ROW][C]63[/C][C]10.7[/C][C]12.109[/C][C]-1.409[/C][/ROW]
[ROW][C]64[/C][C]8.3[/C][C]10.3634[/C][C]-2.06342[/C][/ROW]
[ROW][C]65[/C][C]9.6[/C][C]12.1522[/C][C]-2.55223[/C][/ROW]
[ROW][C]66[/C][C]14.2[/C][C]14.1378[/C][C]0.0621849[/C][/ROW]
[ROW][C]67[/C][C]11.1[/C][C]11.9463[/C][C]-0.846308[/C][/ROW]
[ROW][C]68[/C][C]4.35[/C][C]12.3591[/C][C]-8.00906[/C][/ROW]
[ROW][C]69[/C][C]12.7[/C][C]11.0842[/C][C]1.6158[/C][/ROW]
[ROW][C]70[/C][C]18.1[/C][C]13.7667[/C][C]4.33325[/C][/ROW]
[ROW][C]71[/C][C]17.85[/C][C]12.3477[/C][C]5.50231[/C][/ROW]
[ROW][C]72[/C][C]12.6[/C][C]12.183[/C][C]0.416978[/C][/ROW]
[ROW][C]73[/C][C]17.1[/C][C]12.6961[/C][C]4.40388[/C][/ROW]
[ROW][C]74[/C][C]16.1[/C][C]13.138[/C][C]2.96198[/C][/ROW]
[ROW][C]75[/C][C]14.7[/C][C]12.311[/C][C]2.389[/C][/ROW]
[ROW][C]76[/C][C]10.6[/C][C]12.8514[/C][C]-2.25139[/C][/ROW]
[ROW][C]77[/C][C]12.6[/C][C]12.4352[/C][C]0.16476[/C][/ROW]
[ROW][C]78[/C][C]16.2[/C][C]12.0857[/C][C]4.11427[/C][/ROW]
[ROW][C]79[/C][C]13.6[/C][C]11.6763[/C][C]1.9237[/C][/ROW]
[ROW][C]80[/C][C]18.9[/C][C]12.8394[/C][C]6.06063[/C][/ROW]
[ROW][C]81[/C][C]14.1[/C][C]12.2475[/C][C]1.8525[/C][/ROW]
[ROW][C]82[/C][C]14.5[/C][C]11.805[/C][C]2.69499[/C][/ROW]
[ROW][C]83[/C][C]14.75[/C][C]12.4962[/C][C]2.25381[/C][/ROW]
[ROW][C]84[/C][C]14.8[/C][C]13.2326[/C][C]1.56739[/C][/ROW]
[ROW][C]85[/C][C]12.45[/C][C]11.6626[/C][C]0.787359[/C][/ROW]
[ROW][C]86[/C][C]12.65[/C][C]12.3752[/C][C]0.274807[/C][/ROW]
[ROW][C]87[/C][C]17.35[/C][C]12.04[/C][C]5.31005[/C][/ROW]
[ROW][C]88[/C][C]8.6[/C][C]13.3121[/C][C]-4.71211[/C][/ROW]
[ROW][C]89[/C][C]16.1[/C][C]14.1232[/C][C]1.9768[/C][/ROW]
[ROW][C]90[/C][C]11.6[/C][C]12.326[/C][C]-0.72603[/C][/ROW]
[ROW][C]91[/C][C]17.75[/C][C]13.4131[/C][C]4.33688[/C][/ROW]
[ROW][C]92[/C][C]15.25[/C][C]12.9195[/C][C]2.33051[/C][/ROW]
[ROW][C]93[/C][C]17.65[/C][C]14.1265[/C][C]3.52351[/C][/ROW]
[ROW][C]94[/C][C]13.6[/C][C]14.6806[/C][C]-1.08056[/C][/ROW]
[ROW][C]95[/C][C]18.25[/C][C]12.132[/C][C]6.11803[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]13.0727[/C][C]2.92733[/C][/ROW]
[ROW][C]97[/C][C]18.25[/C][C]13.5542[/C][C]4.69579[/C][/ROW]
[ROW][C]98[/C][C]14.6[/C][C]12.5959[/C][C]2.00408[/C][/ROW]
[ROW][C]99[/C][C]13.85[/C][C]12.3564[/C][C]1.49355[/C][/ROW]
[ROW][C]100[/C][C]18.95[/C][C]13.056[/C][C]5.89402[/C][/ROW]
[ROW][C]101[/C][C]15.9[/C][C]12.0095[/C][C]3.89049[/C][/ROW]
[ROW][C]102[/C][C]16.1[/C][C]13.3057[/C][C]2.7943[/C][/ROW]
[ROW][C]103[/C][C]10.95[/C][C]12.2381[/C][C]-1.28809[/C][/ROW]
[ROW][C]104[/C][C]15.1[/C][C]13.8021[/C][C]1.29789[/C][/ROW]
[ROW][C]105[/C][C]15.95[/C][C]13.6195[/C][C]2.33052[/C][/ROW]
[ROW][C]106[/C][C]14.6[/C][C]12.6675[/C][C]1.93252[/C][/ROW]
[ROW][C]107[/C][C]15.4[/C][C]13.0187[/C][C]2.38131[/C][/ROW]
[ROW][C]108[/C][C]15.4[/C][C]12.8328[/C][C]2.56718[/C][/ROW]
[ROW][C]109[/C][C]17.6[/C][C]12.1403[/C][C]5.45973[/C][/ROW]
[ROW][C]110[/C][C]13.35[/C][C]12.6988[/C][C]0.651204[/C][/ROW]
[ROW][C]111[/C][C]15.35[/C][C]12.4532[/C][C]2.89678[/C][/ROW]
[ROW][C]112[/C][C]19.1[/C][C]14.3492[/C][C]4.75078[/C][/ROW]
[ROW][C]113[/C][C]12.9[/C][C]11.9166[/C][C]0.983365[/C][/ROW]
[ROW][C]114[/C][C]12.6[/C][C]12.7824[/C][C]-0.182383[/C][/ROW]
[ROW][C]115[/C][C]10.35[/C][C]11.9893[/C][C]-1.63935[/C][/ROW]
[ROW][C]116[/C][C]15.4[/C][C]12.7595[/C][C]2.64052[/C][/ROW]
[ROW][C]117[/C][C]9.6[/C][C]11.9801[/C][C]-2.38008[/C][/ROW]
[ROW][C]118[/C][C]14.85[/C][C]12.9348[/C][C]1.91518[/C][/ROW]
[ROW][C]119[/C][C]19.25[/C][C]14.0962[/C][C]5.15381[/C][/ROW]
[ROW][C]120[/C][C]13.6[/C][C]13.0996[/C][C]0.500397[/C][/ROW]
[ROW][C]121[/C][C]12.75[/C][C]11.9787[/C][C]0.771336[/C][/ROW]
[ROW][C]122[/C][C]9.85[/C][C]12.3435[/C][C]-2.49347[/C][/ROW]
[ROW][C]123[/C][C]12.65[/C][C]11.8588[/C][C]0.791222[/C][/ROW]
[ROW][C]124[/C][C]11.9[/C][C]11.5972[/C][C]0.302826[/C][/ROW]
[ROW][C]125[/C][C]16.6[/C][C]12.3238[/C][C]4.27617[/C][/ROW]
[ROW][C]126[/C][C]11.2[/C][C]12.6119[/C][C]-1.41191[/C][/ROW]
[ROW][C]127[/C][C]15.25[/C][C]13.5923[/C][C]1.65773[/C][/ROW]
[ROW][C]128[/C][C]12.4[/C][C]12.6425[/C][C]-0.242532[/C][/ROW]
[ROW][C]129[/C][C]15.85[/C][C]11.6797[/C][C]4.17031[/C][/ROW]
[ROW][C]130[/C][C]18.15[/C][C]12.5986[/C][C]5.55135[/C][/ROW]
[ROW][C]131[/C][C]11.15[/C][C]11.7711[/C][C]-0.62112[/C][/ROW]
[ROW][C]132[/C][C]12.35[/C][C]13.2702[/C][C]-0.920197[/C][/ROW]
[ROW][C]133[/C][C]15.6[/C][C]12.4028[/C][C]3.19719[/C][/ROW]
[ROW][C]134[/C][C]15.6[/C][C]12.3882[/C][C]3.21178[/C][/ROW]
[ROW][C]135[/C][C]18.4[/C][C]12.7769[/C][C]5.62311[/C][/ROW]
[ROW][C]136[/C][C]13.1[/C][C]12.3913[/C][C]0.708657[/C][/ROW]
[ROW][C]137[/C][C]12.85[/C][C]12.1849[/C][C]0.665093[/C][/ROW]
[ROW][C]138[/C][C]9.5[/C][C]13.5028[/C][C]-4.00282[/C][/ROW]
[ROW][C]139[/C][C]4.5[/C][C]12.8178[/C][C]-8.31781[/C][/ROW]
[ROW][C]140[/C][C]13.6[/C][C]13.0289[/C][C]0.571126[/C][/ROW]
[ROW][C]141[/C][C]11.7[/C][C]12.596[/C][C]-0.896041[/C][/ROW]
[ROW][C]142[/C][C]12.4[/C][C]12.1948[/C][C]0.205242[/C][/ROW]
[ROW][C]143[/C][C]14.9[/C][C]12.6494[/C][C]2.25055[/C][/ROW]
[ROW][C]144[/C][C]17.75[/C][C]12.6499[/C][C]5.10011[/C][/ROW]
[ROW][C]145[/C][C]11.2[/C][C]12.8125[/C][C]-1.61254[/C][/ROW]
[ROW][C]146[/C][C]14.6[/C][C]12.0508[/C][C]2.54924[/C][/ROW]
[ROW][C]147[/C][C]14.05[/C][C]10.9766[/C][C]3.07339[/C][/ROW]
[ROW][C]148[/C][C]13.35[/C][C]13.4578[/C][C]-0.107757[/C][/ROW]
[ROW][C]149[/C][C]11.85[/C][C]12.8483[/C][C]-0.998273[/C][/ROW]
[ROW][C]150[/C][C]14.75[/C][C]12.5492[/C][C]2.20081[/C][/ROW]
[ROW][C]151[/C][C]13.2[/C][C]12.3601[/C][C]0.839854[/C][/ROW]
[ROW][C]152[/C][C]7.85[/C][C]12.1132[/C][C]-4.26318[/C][/ROW]
[ROW][C]153[/C][C]7.85[/C][C]11.6242[/C][C]-3.77422[/C][/ROW]
[ROW][C]154[/C][C]10.95[/C][C]12.1899[/C][C]-1.23986[/C][/ROW]
[ROW][C]155[/C][C]9.95[/C][C]12.0725[/C][C]-2.12248[/C][/ROW]
[ROW][C]156[/C][C]14.9[/C][C]12.1648[/C][C]2.7352[/C][/ROW]
[ROW][C]157[/C][C]13.4[/C][C]12.8355[/C][C]0.564526[/C][/ROW]
[ROW][C]158[/C][C]16.85[/C][C]13.4756[/C][C]3.37444[/C][/ROW]
[ROW][C]159[/C][C]10.95[/C][C]12.7518[/C][C]-1.80181[/C][/ROW]
[ROW][C]160[/C][C]12.2[/C][C]13.1469[/C][C]-0.94694[/C][/ROW]
[ROW][C]161[/C][C]15.2[/C][C]12.7453[/C][C]2.45469[/C][/ROW]
[ROW][C]162[/C][C]8.1[/C][C]12.5219[/C][C]-4.42188[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280338&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280338&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
112.211.96590.234051
27.413.2816-5.88163
36.712.6985-5.99852
412.612.24080.359179
513.312.95890.341098
611.112.9204-1.82041
78.213.0918-4.89182
811.412.2825-0.882455
96.412.4957-6.09571
1010.612.0717-1.47169
1111.912.3833-0.483327
129.612.4773-2.87731
136.413.4995-7.09955
1413.812.39791.40207
1513.812.27391.52606
1611.713.0728-1.37275
1710.912.4385-1.53855
1816.111.36884.73119
199.912.7636-2.86356
206.114.0952-7.99525
21911.861-2.86103
229.79.88691-0.186912
2310.812.5953-1.79525
2410.312.8394-2.53937
2512.712.61510.0849052
269.312.6405-3.34052
275.912.3299-6.42989
2811.412.6559-1.25592
291311.99671.0033
3010.89.91740.8826
3112.310.68041.61957
3211.813.6464-1.84636
337.912.2198-4.3198
3412.313.8378-1.5378
3511.612.6778-1.07778
366.712.0573-5.35731
3710.911.9873-1.08728
3812.112.8665-0.766492
3913.313.17050.12954
4010.113.1047-3.00467
4114.312.37561.92438
4213.312.78690.513127
439.314.03-4.72996
4415.912.39443.50556
459.111.8499-2.7499
461312.71380.286199
4714.511.50762.9924
4814.612.59232.00772
497.312.3699-5.06989
5012.612.20120.398796
517.712.226-4.52601
524.311.3972-7.09723
5311.812.6116-0.811599
5411.211.8901-0.690091
5512.612.37940.220648
565.611.7076-6.10761
579.912.3227-2.42269
587.712.8144-5.11439
597.312.6889-5.38888
6011.412.6022-1.20219
6113.611.93521.66484
627.912.7852-4.88518
6310.712.109-1.409
648.310.3634-2.06342
659.612.1522-2.55223
6614.214.13780.0621849
6711.111.9463-0.846308
684.3512.3591-8.00906
6912.711.08421.6158
7018.113.76674.33325
7117.8512.34775.50231
7212.612.1830.416978
7317.112.69614.40388
7416.113.1382.96198
7514.712.3112.389
7610.612.8514-2.25139
7712.612.43520.16476
7816.212.08574.11427
7913.611.67631.9237
8018.912.83946.06063
8114.112.24751.8525
8214.511.8052.69499
8314.7512.49622.25381
8414.813.23261.56739
8512.4511.66260.787359
8612.6512.37520.274807
8717.3512.045.31005
888.613.3121-4.71211
8916.114.12321.9768
9011.612.326-0.72603
9117.7513.41314.33688
9215.2512.91952.33051
9317.6514.12653.52351
9413.614.6806-1.08056
9518.2512.1326.11803
961613.07272.92733
9718.2513.55424.69579
9814.612.59592.00408
9913.8512.35641.49355
10018.9513.0565.89402
10115.912.00953.89049
10216.113.30572.7943
10310.9512.2381-1.28809
10415.113.80211.29789
10515.9513.61952.33052
10614.612.66751.93252
10715.413.01872.38131
10815.412.83282.56718
10917.612.14035.45973
11013.3512.69880.651204
11115.3512.45322.89678
11219.114.34924.75078
11312.911.91660.983365
11412.612.7824-0.182383
11510.3511.9893-1.63935
11615.412.75952.64052
1179.611.9801-2.38008
11814.8512.93481.91518
11919.2514.09625.15381
12013.613.09960.500397
12112.7511.97870.771336
1229.8512.3435-2.49347
12312.6511.85880.791222
12411.911.59720.302826
12516.612.32384.27617
12611.212.6119-1.41191
12715.2513.59231.65773
12812.412.6425-0.242532
12915.8511.67974.17031
13018.1512.59865.55135
13111.1511.7711-0.62112
13212.3513.2702-0.920197
13315.612.40283.19719
13415.612.38823.21178
13518.412.77695.62311
13613.112.39130.708657
13712.8512.18490.665093
1389.513.5028-4.00282
1394.512.8178-8.31781
14013.613.02890.571126
14111.712.596-0.896041
14212.412.19480.205242
14314.912.64942.25055
14417.7512.64995.10011
14511.212.8125-1.61254
14614.612.05082.54924
14714.0510.97663.07339
14813.3513.4578-0.107757
14911.8512.8483-0.998273
15014.7512.54922.20081
15113.212.36010.839854
1527.8512.1132-4.26318
1537.8511.6242-3.77422
15410.9512.1899-1.23986
1559.9512.0725-2.12248
15614.912.16482.7352
15713.412.83550.564526
15816.8513.47563.37444
15910.9512.7518-1.80181
16012.213.1469-0.94694
16115.212.74532.45469
1628.112.5219-4.42188






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.6918440.6163120.308156
110.5352330.9295340.464767
120.4313180.8626360.568682
130.4152340.8304680.584766
140.3623320.7246630.637668
150.2906630.5813260.709337
160.2620360.5240720.737964
170.1955710.3911410.804429
180.1642050.3284110.835795
190.1147770.2295540.885223
200.09205640.1841130.907944
210.1033150.2066290.896685
220.2447520.4895050.755248
230.1879760.3759530.812024
240.1616030.3232050.838397
250.1228850.2457690.877115
260.09593980.191880.90406
270.1552660.3105310.844734
280.13570.2713990.8643
290.1529350.3058690.847065
300.1405650.2811290.859435
310.1131110.2262220.886889
320.09559130.1911830.904409
330.08740230.1748050.912598
340.08577570.1715510.914224
350.06638170.1327630.933618
360.09969460.1993890.900305
370.07774240.1554850.922258
380.06938150.1387630.930618
390.06669070.1333810.933309
400.0554480.1108960.944552
410.05649390.1129880.943506
420.06259540.1251910.937405
430.06138370.1227670.938616
440.09497280.1899460.905027
450.08162870.1632570.918371
460.07784680.1556940.922153
470.09168340.1833670.908317
480.1012950.2025910.898705
490.1276710.2553410.872329
500.1081660.2163320.891834
510.1232040.2464090.876796
520.2668430.5336860.733157
530.2338740.4677490.766126
540.1999470.3998940.800053
550.1721570.3443130.827843
560.2761980.5523960.723802
570.2523630.5047270.747637
580.2809560.5619120.719044
590.3475820.6951640.652418
600.3142150.6284310.685785
610.3080320.6160640.691968
620.3553230.7106460.644677
630.3239450.6478910.676055
640.2996440.5992880.700356
650.2883990.5767980.711601
660.2862570.5725130.713743
670.2538070.5076150.746193
680.5140920.9718160.485908
690.5244940.9510110.475506
700.6307290.7385430.369271
710.755080.4898390.24492
720.7247270.5505460.275273
730.8180740.3638520.181926
740.8300010.3399970.169999
750.8281710.3436580.171829
760.823080.3538390.17692
770.7969330.4061340.203067
780.8289980.3420040.171002
790.8132020.3735970.186798
800.8919750.216050.108025
810.881390.2372190.11861
820.8743510.2512980.125649
830.8643610.2712780.135639
840.8492040.3015920.150796
850.8249460.3501080.175054
860.7956540.4086920.204346
870.8504660.2990680.149534
880.8911520.2176960.108848
890.8870790.2258420.112921
900.8655030.2689930.134497
910.8858840.2282330.114116
920.8729180.2541640.127082
930.872470.2550590.12753
940.8724640.2550730.127536
950.9283560.1432870.0716436
960.9229230.1541530.0770766
970.9360670.1278650.0639327
980.925020.1499610.0749803
990.9096230.1807540.0903771
1000.9388040.1223920.0611959
1010.9439650.1120710.0560354
1020.9369270.1261450.0630727
1030.9258060.1483880.0741938
1040.9092490.1815020.0907508
1050.8943380.2113240.105662
1060.875310.2493790.12469
1070.8626740.2746510.137326
1080.8487010.3025990.151299
1090.8864140.2271710.113586
1100.860390.279220.13961
1110.8509830.2980350.149017
1120.864630.270740.13537
1130.8363650.3272690.163635
1140.8016140.3967720.198386
1150.7793460.4413080.220654
1160.7528420.4943160.247158
1170.7408850.5182290.259115
1180.7057250.5885510.294275
1190.7967270.4065450.203273
1200.7583960.4832080.241604
1210.7134270.5731460.286573
1220.7460480.5079030.253952
1230.6988770.6022450.301123
1240.6636680.6726640.336332
1250.6596370.6807270.340363
1260.624350.7513010.37565
1270.5739310.8521390.426069
1280.5170320.9659360.482968
1290.5753550.849290.424645
1300.6379040.7241930.362096
1310.582810.8343790.41719
1320.5204880.9590240.479512
1330.5136370.9727260.486363
1340.4867520.9735040.513248
1350.5780970.8438050.421903
1360.5098950.9802110.490105
1370.4402850.8805690.559715
1380.5005220.9989550.499478
1390.8851780.2296430.114822
1400.8406760.3186470.159324
1410.8041920.3916160.195808
1420.7390210.5219570.260979
1430.682510.634980.31749
1440.8136250.372750.186375
1450.8030340.3939320.196966
1460.7546770.4906450.245323
1470.8111130.3777750.188887
1480.7476620.5046760.252338
1490.6533860.6932280.346614
1500.6057940.7884120.394206
1510.804160.3916810.19584
1520.7416360.5167280.258364

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.691844 & 0.616312 & 0.308156 \tabularnewline
11 & 0.535233 & 0.929534 & 0.464767 \tabularnewline
12 & 0.431318 & 0.862636 & 0.568682 \tabularnewline
13 & 0.415234 & 0.830468 & 0.584766 \tabularnewline
14 & 0.362332 & 0.724663 & 0.637668 \tabularnewline
15 & 0.290663 & 0.581326 & 0.709337 \tabularnewline
16 & 0.262036 & 0.524072 & 0.737964 \tabularnewline
17 & 0.195571 & 0.391141 & 0.804429 \tabularnewline
18 & 0.164205 & 0.328411 & 0.835795 \tabularnewline
19 & 0.114777 & 0.229554 & 0.885223 \tabularnewline
20 & 0.0920564 & 0.184113 & 0.907944 \tabularnewline
21 & 0.103315 & 0.206629 & 0.896685 \tabularnewline
22 & 0.244752 & 0.489505 & 0.755248 \tabularnewline
23 & 0.187976 & 0.375953 & 0.812024 \tabularnewline
24 & 0.161603 & 0.323205 & 0.838397 \tabularnewline
25 & 0.122885 & 0.245769 & 0.877115 \tabularnewline
26 & 0.0959398 & 0.19188 & 0.90406 \tabularnewline
27 & 0.155266 & 0.310531 & 0.844734 \tabularnewline
28 & 0.1357 & 0.271399 & 0.8643 \tabularnewline
29 & 0.152935 & 0.305869 & 0.847065 \tabularnewline
30 & 0.140565 & 0.281129 & 0.859435 \tabularnewline
31 & 0.113111 & 0.226222 & 0.886889 \tabularnewline
32 & 0.0955913 & 0.191183 & 0.904409 \tabularnewline
33 & 0.0874023 & 0.174805 & 0.912598 \tabularnewline
34 & 0.0857757 & 0.171551 & 0.914224 \tabularnewline
35 & 0.0663817 & 0.132763 & 0.933618 \tabularnewline
36 & 0.0996946 & 0.199389 & 0.900305 \tabularnewline
37 & 0.0777424 & 0.155485 & 0.922258 \tabularnewline
38 & 0.0693815 & 0.138763 & 0.930618 \tabularnewline
39 & 0.0666907 & 0.133381 & 0.933309 \tabularnewline
40 & 0.055448 & 0.110896 & 0.944552 \tabularnewline
41 & 0.0564939 & 0.112988 & 0.943506 \tabularnewline
42 & 0.0625954 & 0.125191 & 0.937405 \tabularnewline
43 & 0.0613837 & 0.122767 & 0.938616 \tabularnewline
44 & 0.0949728 & 0.189946 & 0.905027 \tabularnewline
45 & 0.0816287 & 0.163257 & 0.918371 \tabularnewline
46 & 0.0778468 & 0.155694 & 0.922153 \tabularnewline
47 & 0.0916834 & 0.183367 & 0.908317 \tabularnewline
48 & 0.101295 & 0.202591 & 0.898705 \tabularnewline
49 & 0.127671 & 0.255341 & 0.872329 \tabularnewline
50 & 0.108166 & 0.216332 & 0.891834 \tabularnewline
51 & 0.123204 & 0.246409 & 0.876796 \tabularnewline
52 & 0.266843 & 0.533686 & 0.733157 \tabularnewline
53 & 0.233874 & 0.467749 & 0.766126 \tabularnewline
54 & 0.199947 & 0.399894 & 0.800053 \tabularnewline
55 & 0.172157 & 0.344313 & 0.827843 \tabularnewline
56 & 0.276198 & 0.552396 & 0.723802 \tabularnewline
57 & 0.252363 & 0.504727 & 0.747637 \tabularnewline
58 & 0.280956 & 0.561912 & 0.719044 \tabularnewline
59 & 0.347582 & 0.695164 & 0.652418 \tabularnewline
60 & 0.314215 & 0.628431 & 0.685785 \tabularnewline
61 & 0.308032 & 0.616064 & 0.691968 \tabularnewline
62 & 0.355323 & 0.710646 & 0.644677 \tabularnewline
63 & 0.323945 & 0.647891 & 0.676055 \tabularnewline
64 & 0.299644 & 0.599288 & 0.700356 \tabularnewline
65 & 0.288399 & 0.576798 & 0.711601 \tabularnewline
66 & 0.286257 & 0.572513 & 0.713743 \tabularnewline
67 & 0.253807 & 0.507615 & 0.746193 \tabularnewline
68 & 0.514092 & 0.971816 & 0.485908 \tabularnewline
69 & 0.524494 & 0.951011 & 0.475506 \tabularnewline
70 & 0.630729 & 0.738543 & 0.369271 \tabularnewline
71 & 0.75508 & 0.489839 & 0.24492 \tabularnewline
72 & 0.724727 & 0.550546 & 0.275273 \tabularnewline
73 & 0.818074 & 0.363852 & 0.181926 \tabularnewline
74 & 0.830001 & 0.339997 & 0.169999 \tabularnewline
75 & 0.828171 & 0.343658 & 0.171829 \tabularnewline
76 & 0.82308 & 0.353839 & 0.17692 \tabularnewline
77 & 0.796933 & 0.406134 & 0.203067 \tabularnewline
78 & 0.828998 & 0.342004 & 0.171002 \tabularnewline
79 & 0.813202 & 0.373597 & 0.186798 \tabularnewline
80 & 0.891975 & 0.21605 & 0.108025 \tabularnewline
81 & 0.88139 & 0.237219 & 0.11861 \tabularnewline
82 & 0.874351 & 0.251298 & 0.125649 \tabularnewline
83 & 0.864361 & 0.271278 & 0.135639 \tabularnewline
84 & 0.849204 & 0.301592 & 0.150796 \tabularnewline
85 & 0.824946 & 0.350108 & 0.175054 \tabularnewline
86 & 0.795654 & 0.408692 & 0.204346 \tabularnewline
87 & 0.850466 & 0.299068 & 0.149534 \tabularnewline
88 & 0.891152 & 0.217696 & 0.108848 \tabularnewline
89 & 0.887079 & 0.225842 & 0.112921 \tabularnewline
90 & 0.865503 & 0.268993 & 0.134497 \tabularnewline
91 & 0.885884 & 0.228233 & 0.114116 \tabularnewline
92 & 0.872918 & 0.254164 & 0.127082 \tabularnewline
93 & 0.87247 & 0.255059 & 0.12753 \tabularnewline
94 & 0.872464 & 0.255073 & 0.127536 \tabularnewline
95 & 0.928356 & 0.143287 & 0.0716436 \tabularnewline
96 & 0.922923 & 0.154153 & 0.0770766 \tabularnewline
97 & 0.936067 & 0.127865 & 0.0639327 \tabularnewline
98 & 0.92502 & 0.149961 & 0.0749803 \tabularnewline
99 & 0.909623 & 0.180754 & 0.0903771 \tabularnewline
100 & 0.938804 & 0.122392 & 0.0611959 \tabularnewline
101 & 0.943965 & 0.112071 & 0.0560354 \tabularnewline
102 & 0.936927 & 0.126145 & 0.0630727 \tabularnewline
103 & 0.925806 & 0.148388 & 0.0741938 \tabularnewline
104 & 0.909249 & 0.181502 & 0.0907508 \tabularnewline
105 & 0.894338 & 0.211324 & 0.105662 \tabularnewline
106 & 0.87531 & 0.249379 & 0.12469 \tabularnewline
107 & 0.862674 & 0.274651 & 0.137326 \tabularnewline
108 & 0.848701 & 0.302599 & 0.151299 \tabularnewline
109 & 0.886414 & 0.227171 & 0.113586 \tabularnewline
110 & 0.86039 & 0.27922 & 0.13961 \tabularnewline
111 & 0.850983 & 0.298035 & 0.149017 \tabularnewline
112 & 0.86463 & 0.27074 & 0.13537 \tabularnewline
113 & 0.836365 & 0.327269 & 0.163635 \tabularnewline
114 & 0.801614 & 0.396772 & 0.198386 \tabularnewline
115 & 0.779346 & 0.441308 & 0.220654 \tabularnewline
116 & 0.752842 & 0.494316 & 0.247158 \tabularnewline
117 & 0.740885 & 0.518229 & 0.259115 \tabularnewline
118 & 0.705725 & 0.588551 & 0.294275 \tabularnewline
119 & 0.796727 & 0.406545 & 0.203273 \tabularnewline
120 & 0.758396 & 0.483208 & 0.241604 \tabularnewline
121 & 0.713427 & 0.573146 & 0.286573 \tabularnewline
122 & 0.746048 & 0.507903 & 0.253952 \tabularnewline
123 & 0.698877 & 0.602245 & 0.301123 \tabularnewline
124 & 0.663668 & 0.672664 & 0.336332 \tabularnewline
125 & 0.659637 & 0.680727 & 0.340363 \tabularnewline
126 & 0.62435 & 0.751301 & 0.37565 \tabularnewline
127 & 0.573931 & 0.852139 & 0.426069 \tabularnewline
128 & 0.517032 & 0.965936 & 0.482968 \tabularnewline
129 & 0.575355 & 0.84929 & 0.424645 \tabularnewline
130 & 0.637904 & 0.724193 & 0.362096 \tabularnewline
131 & 0.58281 & 0.834379 & 0.41719 \tabularnewline
132 & 0.520488 & 0.959024 & 0.479512 \tabularnewline
133 & 0.513637 & 0.972726 & 0.486363 \tabularnewline
134 & 0.486752 & 0.973504 & 0.513248 \tabularnewline
135 & 0.578097 & 0.843805 & 0.421903 \tabularnewline
136 & 0.509895 & 0.980211 & 0.490105 \tabularnewline
137 & 0.440285 & 0.880569 & 0.559715 \tabularnewline
138 & 0.500522 & 0.998955 & 0.499478 \tabularnewline
139 & 0.885178 & 0.229643 & 0.114822 \tabularnewline
140 & 0.840676 & 0.318647 & 0.159324 \tabularnewline
141 & 0.804192 & 0.391616 & 0.195808 \tabularnewline
142 & 0.739021 & 0.521957 & 0.260979 \tabularnewline
143 & 0.68251 & 0.63498 & 0.31749 \tabularnewline
144 & 0.813625 & 0.37275 & 0.186375 \tabularnewline
145 & 0.803034 & 0.393932 & 0.196966 \tabularnewline
146 & 0.754677 & 0.490645 & 0.245323 \tabularnewline
147 & 0.811113 & 0.377775 & 0.188887 \tabularnewline
148 & 0.747662 & 0.504676 & 0.252338 \tabularnewline
149 & 0.653386 & 0.693228 & 0.346614 \tabularnewline
150 & 0.605794 & 0.788412 & 0.394206 \tabularnewline
151 & 0.80416 & 0.391681 & 0.19584 \tabularnewline
152 & 0.741636 & 0.516728 & 0.258364 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280338&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]10[/C][C]0.691844[/C][C]0.616312[/C][C]0.308156[/C][/ROW]
[ROW][C]11[/C][C]0.535233[/C][C]0.929534[/C][C]0.464767[/C][/ROW]
[ROW][C]12[/C][C]0.431318[/C][C]0.862636[/C][C]0.568682[/C][/ROW]
[ROW][C]13[/C][C]0.415234[/C][C]0.830468[/C][C]0.584766[/C][/ROW]
[ROW][C]14[/C][C]0.362332[/C][C]0.724663[/C][C]0.637668[/C][/ROW]
[ROW][C]15[/C][C]0.290663[/C][C]0.581326[/C][C]0.709337[/C][/ROW]
[ROW][C]16[/C][C]0.262036[/C][C]0.524072[/C][C]0.737964[/C][/ROW]
[ROW][C]17[/C][C]0.195571[/C][C]0.391141[/C][C]0.804429[/C][/ROW]
[ROW][C]18[/C][C]0.164205[/C][C]0.328411[/C][C]0.835795[/C][/ROW]
[ROW][C]19[/C][C]0.114777[/C][C]0.229554[/C][C]0.885223[/C][/ROW]
[ROW][C]20[/C][C]0.0920564[/C][C]0.184113[/C][C]0.907944[/C][/ROW]
[ROW][C]21[/C][C]0.103315[/C][C]0.206629[/C][C]0.896685[/C][/ROW]
[ROW][C]22[/C][C]0.244752[/C][C]0.489505[/C][C]0.755248[/C][/ROW]
[ROW][C]23[/C][C]0.187976[/C][C]0.375953[/C][C]0.812024[/C][/ROW]
[ROW][C]24[/C][C]0.161603[/C][C]0.323205[/C][C]0.838397[/C][/ROW]
[ROW][C]25[/C][C]0.122885[/C][C]0.245769[/C][C]0.877115[/C][/ROW]
[ROW][C]26[/C][C]0.0959398[/C][C]0.19188[/C][C]0.90406[/C][/ROW]
[ROW][C]27[/C][C]0.155266[/C][C]0.310531[/C][C]0.844734[/C][/ROW]
[ROW][C]28[/C][C]0.1357[/C][C]0.271399[/C][C]0.8643[/C][/ROW]
[ROW][C]29[/C][C]0.152935[/C][C]0.305869[/C][C]0.847065[/C][/ROW]
[ROW][C]30[/C][C]0.140565[/C][C]0.281129[/C][C]0.859435[/C][/ROW]
[ROW][C]31[/C][C]0.113111[/C][C]0.226222[/C][C]0.886889[/C][/ROW]
[ROW][C]32[/C][C]0.0955913[/C][C]0.191183[/C][C]0.904409[/C][/ROW]
[ROW][C]33[/C][C]0.0874023[/C][C]0.174805[/C][C]0.912598[/C][/ROW]
[ROW][C]34[/C][C]0.0857757[/C][C]0.171551[/C][C]0.914224[/C][/ROW]
[ROW][C]35[/C][C]0.0663817[/C][C]0.132763[/C][C]0.933618[/C][/ROW]
[ROW][C]36[/C][C]0.0996946[/C][C]0.199389[/C][C]0.900305[/C][/ROW]
[ROW][C]37[/C][C]0.0777424[/C][C]0.155485[/C][C]0.922258[/C][/ROW]
[ROW][C]38[/C][C]0.0693815[/C][C]0.138763[/C][C]0.930618[/C][/ROW]
[ROW][C]39[/C][C]0.0666907[/C][C]0.133381[/C][C]0.933309[/C][/ROW]
[ROW][C]40[/C][C]0.055448[/C][C]0.110896[/C][C]0.944552[/C][/ROW]
[ROW][C]41[/C][C]0.0564939[/C][C]0.112988[/C][C]0.943506[/C][/ROW]
[ROW][C]42[/C][C]0.0625954[/C][C]0.125191[/C][C]0.937405[/C][/ROW]
[ROW][C]43[/C][C]0.0613837[/C][C]0.122767[/C][C]0.938616[/C][/ROW]
[ROW][C]44[/C][C]0.0949728[/C][C]0.189946[/C][C]0.905027[/C][/ROW]
[ROW][C]45[/C][C]0.0816287[/C][C]0.163257[/C][C]0.918371[/C][/ROW]
[ROW][C]46[/C][C]0.0778468[/C][C]0.155694[/C][C]0.922153[/C][/ROW]
[ROW][C]47[/C][C]0.0916834[/C][C]0.183367[/C][C]0.908317[/C][/ROW]
[ROW][C]48[/C][C]0.101295[/C][C]0.202591[/C][C]0.898705[/C][/ROW]
[ROW][C]49[/C][C]0.127671[/C][C]0.255341[/C][C]0.872329[/C][/ROW]
[ROW][C]50[/C][C]0.108166[/C][C]0.216332[/C][C]0.891834[/C][/ROW]
[ROW][C]51[/C][C]0.123204[/C][C]0.246409[/C][C]0.876796[/C][/ROW]
[ROW][C]52[/C][C]0.266843[/C][C]0.533686[/C][C]0.733157[/C][/ROW]
[ROW][C]53[/C][C]0.233874[/C][C]0.467749[/C][C]0.766126[/C][/ROW]
[ROW][C]54[/C][C]0.199947[/C][C]0.399894[/C][C]0.800053[/C][/ROW]
[ROW][C]55[/C][C]0.172157[/C][C]0.344313[/C][C]0.827843[/C][/ROW]
[ROW][C]56[/C][C]0.276198[/C][C]0.552396[/C][C]0.723802[/C][/ROW]
[ROW][C]57[/C][C]0.252363[/C][C]0.504727[/C][C]0.747637[/C][/ROW]
[ROW][C]58[/C][C]0.280956[/C][C]0.561912[/C][C]0.719044[/C][/ROW]
[ROW][C]59[/C][C]0.347582[/C][C]0.695164[/C][C]0.652418[/C][/ROW]
[ROW][C]60[/C][C]0.314215[/C][C]0.628431[/C][C]0.685785[/C][/ROW]
[ROW][C]61[/C][C]0.308032[/C][C]0.616064[/C][C]0.691968[/C][/ROW]
[ROW][C]62[/C][C]0.355323[/C][C]0.710646[/C][C]0.644677[/C][/ROW]
[ROW][C]63[/C][C]0.323945[/C][C]0.647891[/C][C]0.676055[/C][/ROW]
[ROW][C]64[/C][C]0.299644[/C][C]0.599288[/C][C]0.700356[/C][/ROW]
[ROW][C]65[/C][C]0.288399[/C][C]0.576798[/C][C]0.711601[/C][/ROW]
[ROW][C]66[/C][C]0.286257[/C][C]0.572513[/C][C]0.713743[/C][/ROW]
[ROW][C]67[/C][C]0.253807[/C][C]0.507615[/C][C]0.746193[/C][/ROW]
[ROW][C]68[/C][C]0.514092[/C][C]0.971816[/C][C]0.485908[/C][/ROW]
[ROW][C]69[/C][C]0.524494[/C][C]0.951011[/C][C]0.475506[/C][/ROW]
[ROW][C]70[/C][C]0.630729[/C][C]0.738543[/C][C]0.369271[/C][/ROW]
[ROW][C]71[/C][C]0.75508[/C][C]0.489839[/C][C]0.24492[/C][/ROW]
[ROW][C]72[/C][C]0.724727[/C][C]0.550546[/C][C]0.275273[/C][/ROW]
[ROW][C]73[/C][C]0.818074[/C][C]0.363852[/C][C]0.181926[/C][/ROW]
[ROW][C]74[/C][C]0.830001[/C][C]0.339997[/C][C]0.169999[/C][/ROW]
[ROW][C]75[/C][C]0.828171[/C][C]0.343658[/C][C]0.171829[/C][/ROW]
[ROW][C]76[/C][C]0.82308[/C][C]0.353839[/C][C]0.17692[/C][/ROW]
[ROW][C]77[/C][C]0.796933[/C][C]0.406134[/C][C]0.203067[/C][/ROW]
[ROW][C]78[/C][C]0.828998[/C][C]0.342004[/C][C]0.171002[/C][/ROW]
[ROW][C]79[/C][C]0.813202[/C][C]0.373597[/C][C]0.186798[/C][/ROW]
[ROW][C]80[/C][C]0.891975[/C][C]0.21605[/C][C]0.108025[/C][/ROW]
[ROW][C]81[/C][C]0.88139[/C][C]0.237219[/C][C]0.11861[/C][/ROW]
[ROW][C]82[/C][C]0.874351[/C][C]0.251298[/C][C]0.125649[/C][/ROW]
[ROW][C]83[/C][C]0.864361[/C][C]0.271278[/C][C]0.135639[/C][/ROW]
[ROW][C]84[/C][C]0.849204[/C][C]0.301592[/C][C]0.150796[/C][/ROW]
[ROW][C]85[/C][C]0.824946[/C][C]0.350108[/C][C]0.175054[/C][/ROW]
[ROW][C]86[/C][C]0.795654[/C][C]0.408692[/C][C]0.204346[/C][/ROW]
[ROW][C]87[/C][C]0.850466[/C][C]0.299068[/C][C]0.149534[/C][/ROW]
[ROW][C]88[/C][C]0.891152[/C][C]0.217696[/C][C]0.108848[/C][/ROW]
[ROW][C]89[/C][C]0.887079[/C][C]0.225842[/C][C]0.112921[/C][/ROW]
[ROW][C]90[/C][C]0.865503[/C][C]0.268993[/C][C]0.134497[/C][/ROW]
[ROW][C]91[/C][C]0.885884[/C][C]0.228233[/C][C]0.114116[/C][/ROW]
[ROW][C]92[/C][C]0.872918[/C][C]0.254164[/C][C]0.127082[/C][/ROW]
[ROW][C]93[/C][C]0.87247[/C][C]0.255059[/C][C]0.12753[/C][/ROW]
[ROW][C]94[/C][C]0.872464[/C][C]0.255073[/C][C]0.127536[/C][/ROW]
[ROW][C]95[/C][C]0.928356[/C][C]0.143287[/C][C]0.0716436[/C][/ROW]
[ROW][C]96[/C][C]0.922923[/C][C]0.154153[/C][C]0.0770766[/C][/ROW]
[ROW][C]97[/C][C]0.936067[/C][C]0.127865[/C][C]0.0639327[/C][/ROW]
[ROW][C]98[/C][C]0.92502[/C][C]0.149961[/C][C]0.0749803[/C][/ROW]
[ROW][C]99[/C][C]0.909623[/C][C]0.180754[/C][C]0.0903771[/C][/ROW]
[ROW][C]100[/C][C]0.938804[/C][C]0.122392[/C][C]0.0611959[/C][/ROW]
[ROW][C]101[/C][C]0.943965[/C][C]0.112071[/C][C]0.0560354[/C][/ROW]
[ROW][C]102[/C][C]0.936927[/C][C]0.126145[/C][C]0.0630727[/C][/ROW]
[ROW][C]103[/C][C]0.925806[/C][C]0.148388[/C][C]0.0741938[/C][/ROW]
[ROW][C]104[/C][C]0.909249[/C][C]0.181502[/C][C]0.0907508[/C][/ROW]
[ROW][C]105[/C][C]0.894338[/C][C]0.211324[/C][C]0.105662[/C][/ROW]
[ROW][C]106[/C][C]0.87531[/C][C]0.249379[/C][C]0.12469[/C][/ROW]
[ROW][C]107[/C][C]0.862674[/C][C]0.274651[/C][C]0.137326[/C][/ROW]
[ROW][C]108[/C][C]0.848701[/C][C]0.302599[/C][C]0.151299[/C][/ROW]
[ROW][C]109[/C][C]0.886414[/C][C]0.227171[/C][C]0.113586[/C][/ROW]
[ROW][C]110[/C][C]0.86039[/C][C]0.27922[/C][C]0.13961[/C][/ROW]
[ROW][C]111[/C][C]0.850983[/C][C]0.298035[/C][C]0.149017[/C][/ROW]
[ROW][C]112[/C][C]0.86463[/C][C]0.27074[/C][C]0.13537[/C][/ROW]
[ROW][C]113[/C][C]0.836365[/C][C]0.327269[/C][C]0.163635[/C][/ROW]
[ROW][C]114[/C][C]0.801614[/C][C]0.396772[/C][C]0.198386[/C][/ROW]
[ROW][C]115[/C][C]0.779346[/C][C]0.441308[/C][C]0.220654[/C][/ROW]
[ROW][C]116[/C][C]0.752842[/C][C]0.494316[/C][C]0.247158[/C][/ROW]
[ROW][C]117[/C][C]0.740885[/C][C]0.518229[/C][C]0.259115[/C][/ROW]
[ROW][C]118[/C][C]0.705725[/C][C]0.588551[/C][C]0.294275[/C][/ROW]
[ROW][C]119[/C][C]0.796727[/C][C]0.406545[/C][C]0.203273[/C][/ROW]
[ROW][C]120[/C][C]0.758396[/C][C]0.483208[/C][C]0.241604[/C][/ROW]
[ROW][C]121[/C][C]0.713427[/C][C]0.573146[/C][C]0.286573[/C][/ROW]
[ROW][C]122[/C][C]0.746048[/C][C]0.507903[/C][C]0.253952[/C][/ROW]
[ROW][C]123[/C][C]0.698877[/C][C]0.602245[/C][C]0.301123[/C][/ROW]
[ROW][C]124[/C][C]0.663668[/C][C]0.672664[/C][C]0.336332[/C][/ROW]
[ROW][C]125[/C][C]0.659637[/C][C]0.680727[/C][C]0.340363[/C][/ROW]
[ROW][C]126[/C][C]0.62435[/C][C]0.751301[/C][C]0.37565[/C][/ROW]
[ROW][C]127[/C][C]0.573931[/C][C]0.852139[/C][C]0.426069[/C][/ROW]
[ROW][C]128[/C][C]0.517032[/C][C]0.965936[/C][C]0.482968[/C][/ROW]
[ROW][C]129[/C][C]0.575355[/C][C]0.84929[/C][C]0.424645[/C][/ROW]
[ROW][C]130[/C][C]0.637904[/C][C]0.724193[/C][C]0.362096[/C][/ROW]
[ROW][C]131[/C][C]0.58281[/C][C]0.834379[/C][C]0.41719[/C][/ROW]
[ROW][C]132[/C][C]0.520488[/C][C]0.959024[/C][C]0.479512[/C][/ROW]
[ROW][C]133[/C][C]0.513637[/C][C]0.972726[/C][C]0.486363[/C][/ROW]
[ROW][C]134[/C][C]0.486752[/C][C]0.973504[/C][C]0.513248[/C][/ROW]
[ROW][C]135[/C][C]0.578097[/C][C]0.843805[/C][C]0.421903[/C][/ROW]
[ROW][C]136[/C][C]0.509895[/C][C]0.980211[/C][C]0.490105[/C][/ROW]
[ROW][C]137[/C][C]0.440285[/C][C]0.880569[/C][C]0.559715[/C][/ROW]
[ROW][C]138[/C][C]0.500522[/C][C]0.998955[/C][C]0.499478[/C][/ROW]
[ROW][C]139[/C][C]0.885178[/C][C]0.229643[/C][C]0.114822[/C][/ROW]
[ROW][C]140[/C][C]0.840676[/C][C]0.318647[/C][C]0.159324[/C][/ROW]
[ROW][C]141[/C][C]0.804192[/C][C]0.391616[/C][C]0.195808[/C][/ROW]
[ROW][C]142[/C][C]0.739021[/C][C]0.521957[/C][C]0.260979[/C][/ROW]
[ROW][C]143[/C][C]0.68251[/C][C]0.63498[/C][C]0.31749[/C][/ROW]
[ROW][C]144[/C][C]0.813625[/C][C]0.37275[/C][C]0.186375[/C][/ROW]
[ROW][C]145[/C][C]0.803034[/C][C]0.393932[/C][C]0.196966[/C][/ROW]
[ROW][C]146[/C][C]0.754677[/C][C]0.490645[/C][C]0.245323[/C][/ROW]
[ROW][C]147[/C][C]0.811113[/C][C]0.377775[/C][C]0.188887[/C][/ROW]
[ROW][C]148[/C][C]0.747662[/C][C]0.504676[/C][C]0.252338[/C][/ROW]
[ROW][C]149[/C][C]0.653386[/C][C]0.693228[/C][C]0.346614[/C][/ROW]
[ROW][C]150[/C][C]0.605794[/C][C]0.788412[/C][C]0.394206[/C][/ROW]
[ROW][C]151[/C][C]0.80416[/C][C]0.391681[/C][C]0.19584[/C][/ROW]
[ROW][C]152[/C][C]0.741636[/C][C]0.516728[/C][C]0.258364[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280338&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280338&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
100.6918440.6163120.308156
110.5352330.9295340.464767
120.4313180.8626360.568682
130.4152340.8304680.584766
140.3623320.7246630.637668
150.2906630.5813260.709337
160.2620360.5240720.737964
170.1955710.3911410.804429
180.1642050.3284110.835795
190.1147770.2295540.885223
200.09205640.1841130.907944
210.1033150.2066290.896685
220.2447520.4895050.755248
230.1879760.3759530.812024
240.1616030.3232050.838397
250.1228850.2457690.877115
260.09593980.191880.90406
270.1552660.3105310.844734
280.13570.2713990.8643
290.1529350.3058690.847065
300.1405650.2811290.859435
310.1131110.2262220.886889
320.09559130.1911830.904409
330.08740230.1748050.912598
340.08577570.1715510.914224
350.06638170.1327630.933618
360.09969460.1993890.900305
370.07774240.1554850.922258
380.06938150.1387630.930618
390.06669070.1333810.933309
400.0554480.1108960.944552
410.05649390.1129880.943506
420.06259540.1251910.937405
430.06138370.1227670.938616
440.09497280.1899460.905027
450.08162870.1632570.918371
460.07784680.1556940.922153
470.09168340.1833670.908317
480.1012950.2025910.898705
490.1276710.2553410.872329
500.1081660.2163320.891834
510.1232040.2464090.876796
520.2668430.5336860.733157
530.2338740.4677490.766126
540.1999470.3998940.800053
550.1721570.3443130.827843
560.2761980.5523960.723802
570.2523630.5047270.747637
580.2809560.5619120.719044
590.3475820.6951640.652418
600.3142150.6284310.685785
610.3080320.6160640.691968
620.3553230.7106460.644677
630.3239450.6478910.676055
640.2996440.5992880.700356
650.2883990.5767980.711601
660.2862570.5725130.713743
670.2538070.5076150.746193
680.5140920.9718160.485908
690.5244940.9510110.475506
700.6307290.7385430.369271
710.755080.4898390.24492
720.7247270.5505460.275273
730.8180740.3638520.181926
740.8300010.3399970.169999
750.8281710.3436580.171829
760.823080.3538390.17692
770.7969330.4061340.203067
780.8289980.3420040.171002
790.8132020.3735970.186798
800.8919750.216050.108025
810.881390.2372190.11861
820.8743510.2512980.125649
830.8643610.2712780.135639
840.8492040.3015920.150796
850.8249460.3501080.175054
860.7956540.4086920.204346
870.8504660.2990680.149534
880.8911520.2176960.108848
890.8870790.2258420.112921
900.8655030.2689930.134497
910.8858840.2282330.114116
920.8729180.2541640.127082
930.872470.2550590.12753
940.8724640.2550730.127536
950.9283560.1432870.0716436
960.9229230.1541530.0770766
970.9360670.1278650.0639327
980.925020.1499610.0749803
990.9096230.1807540.0903771
1000.9388040.1223920.0611959
1010.9439650.1120710.0560354
1020.9369270.1261450.0630727
1030.9258060.1483880.0741938
1040.9092490.1815020.0907508
1050.8943380.2113240.105662
1060.875310.2493790.12469
1070.8626740.2746510.137326
1080.8487010.3025990.151299
1090.8864140.2271710.113586
1100.860390.279220.13961
1110.8509830.2980350.149017
1120.864630.270740.13537
1130.8363650.3272690.163635
1140.8016140.3967720.198386
1150.7793460.4413080.220654
1160.7528420.4943160.247158
1170.7408850.5182290.259115
1180.7057250.5885510.294275
1190.7967270.4065450.203273
1200.7583960.4832080.241604
1210.7134270.5731460.286573
1220.7460480.5079030.253952
1230.6988770.6022450.301123
1240.6636680.6726640.336332
1250.6596370.6807270.340363
1260.624350.7513010.37565
1270.5739310.8521390.426069
1280.5170320.9659360.482968
1290.5753550.849290.424645
1300.6379040.7241930.362096
1310.582810.8343790.41719
1320.5204880.9590240.479512
1330.5136370.9727260.486363
1340.4867520.9735040.513248
1350.5780970.8438050.421903
1360.5098950.9802110.490105
1370.4402850.8805690.559715
1380.5005220.9989550.499478
1390.8851780.2296430.114822
1400.8406760.3186470.159324
1410.8041920.3916160.195808
1420.7390210.5219570.260979
1430.682510.634980.31749
1440.8136250.372750.186375
1450.8030340.3939320.196966
1460.7546770.4906450.245323
1470.8111130.3777750.188887
1480.7476620.5046760.252338
1490.6533860.6932280.346614
1500.6057940.7884120.394206
1510.804160.3916810.19584
1520.7416360.5167280.258364






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 level00OK

\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 & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280338&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280338&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280338&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 level00OK


Figures (Output of Computation)

Figure 1
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Figure 7
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 7 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 7 ; 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')
}