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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 computationSun, 23 Aug 2015 02:02:22 +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/23/t1440291784kovbr1fb2jnjgl2.htm/, Retrieved Wed, 15 May 2024 16:41:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=280313, Retrieved Wed, 15 May 2024 16:41:13 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact138

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 01:02:22] [3e99441ea7f7f69c8fa4628f6be951c3] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
11	8	7	18	12	20	12,9
15	18	18	23	20	25	7,4
19	18	20	23	20	19	12,2
16	12	9	22	14	18	12,8
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	16	17	28	21	24	14,8
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
24	22	12	26	24	23	12
25	25	18	28	23	24	6,3
15	16	12	20	21	25	11,9
21	19	16	26	20	24	9,3
27	26	21	28	25	28	10
26	24	15	27	16	28	6,4
26	20	17	23	24	22	13,8
22	19	17	24	21	26	10,8
21	19	17	24	22	26	13,8
22	23	18	22	25	21	11,7
20	18	15	21	23	26	10,9
22	21	21	21	22	24	9,9
21	20	12	26	25	25	11,5
8	15	6	23	23	24	8,3
22	19	13	21	19	20	11,7
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
23	19	12	23	24	23	10,4
22	20	19	22	18	18	9,3
19	18	10	24	28	24	11,8
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
24	22	22	23	22	22	11,3
17	20	12	17	19	23	11,8
25	22	20	25	26	25	12,7
18	14	11	24	12	24	10,9
24	21	17	20	20	23	13,3
23	20	14	19	24	27	10,1
20	18	16	21	22	23	14,3
22	24	15	18	23	23	9,3
22	19	15	27	19	24	12,5
15	16	10	25	24	26	7,6
17	16	10	20	21	20	15,9
19	16	18	21	16	23	9,2
22	22	22	27	23	23	11,1
21	21	16	24	20	17	13
21	15	10	27	19	20	14,5
20	15	16	23	18	18	12,3
21	14	16	24	21	19	11,4
15	17	13	22	20	24	7,3
18	14	5	27	25	26	12,6
16	16	10	25	17	25	13
24	26	16	24	24	18	13,2
19	18	16	23	22	26	7,7
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
21	17	12	24	9	22	17,1
23	20	16	28	15	20	19,1
20	23	17	23	23	21	16,1
20	18	14	19	21	25	13,35
18	13	13	19	9	19	18,4
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
20	16	15	19	21	23	14,1
17	15	16	20	21	19	14,5
22	19	19	26	21	21	16,15
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
21	22	16	22	24	25	18,4
25	24	12	21	15	15	16,1
19	21	7	24	20	26	17,75
24	25	20	26	26	21	15,25
17	26	9	24	26	26	17,65
22	19	14	26	18	15	15,6
22	21	12	22	23	23	16,35
17	14	10	20	13	15	17,65
26	28	19	20	16	16	13,6
19	16	16	20	19	20	11,7
20	21	11	18	22	20	14,35
19	16	15	18	21	20	14,75
21	16	14	25	11	21	18,25
24	25	11	28	23	28	9,9
21	21	14	23	18	19	16
19	22	15	20	19	21	18,25
13	9	7	22	15	22	16,85
27	24	22	23	21	17	18,95
22	22	11	20	25	26	15,6
21	10	12	24	12	22	17,1
22	22	17	18	24	17	16,1
22	21	13	23	19	16	15,4
21	20	15	21	21	18	15,4
19	17	11	19	19	17	13,35
11	7	7	19	18	25	19,1
19	14	13	25	23	21	7,6
21	23	7	18	23	27	19,1
19	18	11	22	27	23	14,75
8	17	22	5	6	8	19,25
23	20	15	24	22	22	13,6
17	19	15	28	23	28	12,75
25	19	11	27	20	24	9,85
24	23	10	23	23	25	15,25
22	20	18	24	27	23	11,9
23	19	14	25	24	26	16,35
17	16	16	19	12	22	12,4
24	11	8	14	16	20	14,35
22	21	16	24	24	22	18,15
21	20	17	28	24	26	17,75
19	20	14	19	19	21	12,35
19	19	10	23	28	21	15,6
16	19	16	23	23	24	19,3
23	20	16	26	19	18	17,1
23	22	17	25	23	26	18,4
20	19	12	24	20	23	19,05
24	23	17	23	18	25	18,55
25	16	11	22	20	20	19,1
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
27	26	18	22	26	21	13,35
22	19	13	21	22	17	17,75
16	13	14	26	12	19	17,6
18	10	13	24	12	24	14,05
23	21	19	27	20	21	16,1
24	24	15	22	25	21	13,35
20	21	15	23	24	26	11,85
20	23	8	22	23	24	11,95
14	16	11	20	22	23	13,2
23	26	17	27	28	26	7,7
16	16	12	20	15	20	14,6

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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 & 5 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.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=280313&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.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=280313&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280313&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 time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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] = + 17.6651 + 0.0736781I1[t] + 0.00639367I2[t] -0.0813661I3[t] -0.120554E1[t] -0.0614221E2[t] -0.0452966E3[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT
[t] =  +  17.6651 +  0.0736781I1[t] +  0.00639367I2[t] -0.0813661I3[t] -0.120554E1[t] -0.0614221E2[t] -0.0452966E3[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280313&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT
[t] =  +  17.6651 +  0.0736781I1[t] +  0.00639367I2[t] -0.0813661I3[t] -0.120554E1[t] -0.0614221E2[t] -0.0452966E3[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280313&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280313&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] = + 17.6651 + 0.0736781I1[t] + 0.00639367I2[t] -0.0813661I3[t] -0.120554E1[t] -0.0614221E2[t] -0.0452966E3[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)17.66512.522117.0049.157e-114.5785e-11
I10.07367810.1037010.71050.4785680.239284
I20.006393670.100590.063560.9494090.474704
I3-0.08136610.0837337-0.97170.332840.16642
E1-0.1205540.105454-1.1430.2548850.127443
E2-0.06142210.0857357-0.71640.4749140.237457
E3-0.04529660.0951099-0.47630.6346250.317312

\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) & 17.6651 & 2.52211 & 7.004 & 9.157e-11 & 4.5785e-11 \tabularnewline
I1 & 0.0736781 & 0.103701 & 0.7105 & 0.478568 & 0.239284 \tabularnewline
I2 & 0.00639367 & 0.10059 & 0.06356 & 0.949409 & 0.474704 \tabularnewline
I3 & -0.0813661 & 0.0837337 & -0.9717 & 0.33284 & 0.16642 \tabularnewline
E1 & -0.120554 & 0.105454 & -1.143 & 0.254885 & 0.127443 \tabularnewline
E2 & -0.0614221 & 0.0857357 & -0.7164 & 0.474914 & 0.237457 \tabularnewline
E3 & -0.0452966 & 0.0951099 & -0.4763 & 0.634625 & 0.317312 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280313&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]17.6651[/C][C]2.52211[/C][C]7.004[/C][C]9.157e-11[/C][C]4.5785e-11[/C][/ROW]
[ROW][C]I1[/C][C]0.0736781[/C][C]0.103701[/C][C]0.7105[/C][C]0.478568[/C][C]0.239284[/C][/ROW]
[ROW][C]I2[/C][C]0.00639367[/C][C]0.10059[/C][C]0.06356[/C][C]0.949409[/C][C]0.474704[/C][/ROW]
[ROW][C]I3[/C][C]-0.0813661[/C][C]0.0837337[/C][C]-0.9717[/C][C]0.33284[/C][C]0.16642[/C][/ROW]
[ROW][C]E1[/C][C]-0.120554[/C][C]0.105454[/C][C]-1.143[/C][C]0.254885[/C][C]0.127443[/C][/ROW]
[ROW][C]E2[/C][C]-0.0614221[/C][C]0.0857357[/C][C]-0.7164[/C][C]0.474914[/C][C]0.237457[/C][/ROW]
[ROW][C]E3[/C][C]-0.0452966[/C][C]0.0951099[/C][C]-0.4763[/C][C]0.634625[/C][C]0.317312[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280313&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280313&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)17.66512.522117.0049.157e-114.5785e-11
I10.07367810.1037010.71050.4785680.239284
I20.006393670.100590.063560.9494090.474704
I3-0.08136610.0837337-0.97170.332840.16642
E1-0.1205540.105454-1.1430.2548850.127443
E2-0.06142210.0857357-0.71640.4749140.237457
E3-0.04529660.0951099-0.47630.6346250.317312






Multiple Linear Regression - Regression Statistics
Multiple R0.189969
R-squared0.0360883
Adjusted R-squared-0.00464041
F-TEST (value)0.886065
F-TEST (DF numerator)6
F-TEST (DF denominator)142
p-value0.506915
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.5535
Sum Squared Residuals1793.09

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.189969 \tabularnewline
R-squared & 0.0360883 \tabularnewline
Adjusted R-squared & -0.00464041 \tabularnewline
F-TEST (value) & 0.886065 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 142 \tabularnewline
p-value & 0.506915 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.5535 \tabularnewline
Sum Squared Residuals & 1793.09 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280313&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.189969[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0360883[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.00464041[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.886065[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]142[/C][/ROW]
[ROW][C]p-value[/C][C]0.506915[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.5535[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1793.09[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280313&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280313&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 R0.189969
R-squared0.0360883
Adjusted R-squared-0.00464041
F-TEST (value)0.886065
F-TEST (DF numerator)6
F-TEST (DF denominator)142
p-value0.506915
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.5535
Sum Squared Residuals1793.09






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.914.1442-1.24419
27.412.2872-4.88718
312.212.6909-0.490945
412.813.861-1.06096
57.412.766-5.36602
66.713.7784-7.0784
712.612.5890.0109581
814.812.03162.76842
913.314.6272-1.32718
1011.111.2652-0.165241
118.214.4274-6.22745
1211.412.5835-1.18352
136.412.8261-6.42614
1410.611.8075-1.20752
151212.9473-0.947302
166.312.327-6.02698
1711.913.0628-1.16283
189.312.582-3.28201
191011.9326-1.9326
206.413.0077-6.60769
2113.813.0820.718001
2210.812.6634-1.86342
2313.812.52831.27168
2411.712.8295-1.12953
2510.912.9112-2.01122
269.912.7416-2.84157
2711.512.5615-1.06147
288.312.5897-4.28968
2911.713.7452-2.04517
306.112.7206-6.62058
31912.0823-3.08226
329.713.1885-3.48847
3310.812.5563-1.75628
3410.313.261-2.96096
3510.413.2161-2.8161
369.313.2948-3.99483
3711.812.6662-0.866192
385.914.0189-8.11894
3911.413.8253-2.4253
401313.0973-0.0973222
4110.812.4119-1.61189
4211.312.6634-1.36344
4311.813.8109-2.01086
4412.712.27720.422831
4510.913.4683-2.56833
4613.313.5031-0.20309
4710.113.3608-3.2608
4814.313.02721.27284
499.313.5945-4.29449
5012.512.6779-0.177927
517.612.3932-4.79323
5215.913.59942.3006
539.213.1465-3.9465
5411.111.9272-0.827154
551313.153-0.152985
5614.513.16671.33331
5712.313.239-0.939047
5811.412.9562-1.55621
597.312.8535-5.55347
6012.612.7058-0.105781
611312.94220.0578376
6213.213.1150.0849968
637.712.5765-4.87649
644.3513.8353-9.48533
6512.713.3967-0.696666
6618.113.31084.78925
6717.8512.30915.54093
6817.113.9023.19797
6919.112.9836.11705
7016.112.76583.33417
7113.3513.4018-0.0518329
7218.414.31274.08728
7314.712.15892.54109
7410.612.7068-2.10681
7512.612.6867-0.086689
7616.213.24482.95521
7713.612.97050.629497
7814.113.39830.701727
7914.513.15011.34989
8016.1512.48613.66394
8114.7513.05941.6906
8214.812.45752.34252
8312.4514.046-1.59601
8412.6512.6743-0.02425
8517.3513.19544.15463
868.613.279-4.67904
8718.412.79245.60757
8816.114.55171.54829
8917.7513.33034.41975
9015.2512.28332.9667
9117.6512.68364.9664
9215.613.34892.25106
9316.3513.33723.01281
9417.6514.30453.34552
9513.614.0952-0.495234
9611.713.3814-1.68141
9714.3513.95070.399273
9814.7513.5811.16896
9918.2513.53484.71519
1009.912.6417-2.74168
1011613.46852.53148
10218.2513.45584.79416
10316.8513.54093.30913
10418.9513.18525.76483
10515.613.40732.19268
10617.113.6733.42699
10716.113.62932.47067
10815.413.6981.70197
10915.413.48291.9171
11013.3514.0511-0.701076
11119.113.42225.67777
1127.612.719-5.11897
11319.113.98425.11584
11414.7512.93271.81735
11519.2515.23954.0105
11613.613.0260.574013
11712.7511.76210.987892
1189.8513.163-3.313
11915.2513.44891.80108
12011.912.3558-0.455804
12116.3512.67643.67362
12212.413.694-1.29397
12314.3515.2763-0.926348
12418.1512.75455.39551
12517.7511.92975.82035
12612.3513.645-1.29497
12715.612.9292.67097
12819.312.3916.90898
12917.113.0694.03103
13018.412.51295.88712
13119.0513.12025.9298
13218.5513.18655.36353
13319.113.92785.17222
13412.8512.67540.174608
1359.513.6869-4.18691
1364.513.248-8.74799
13713.612.66440.935582
13811.715.1151-3.41513
13913.3513.15570.194321
14017.7513.69684.05321
14117.613.05594.54415
14214.0513.280.769981
14316.112.51343.5866
14413.3513.22740.122622
14511.8512.6279-0.77787
14611.9513.4828-1.53279
14713.213.09970.100308
1487.711.9902-4.29024
14914.613.73150.868474

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 14.1442 & -1.24419 \tabularnewline
2 & 7.4 & 12.2872 & -4.88718 \tabularnewline
3 & 12.2 & 12.6909 & -0.490945 \tabularnewline
4 & 12.8 & 13.861 & -1.06096 \tabularnewline
5 & 7.4 & 12.766 & -5.36602 \tabularnewline
6 & 6.7 & 13.7784 & -7.0784 \tabularnewline
7 & 12.6 & 12.589 & 0.0109581 \tabularnewline
8 & 14.8 & 12.0316 & 2.76842 \tabularnewline
9 & 13.3 & 14.6272 & -1.32718 \tabularnewline
10 & 11.1 & 11.2652 & -0.165241 \tabularnewline
11 & 8.2 & 14.4274 & -6.22745 \tabularnewline
12 & 11.4 & 12.5835 & -1.18352 \tabularnewline
13 & 6.4 & 12.8261 & -6.42614 \tabularnewline
14 & 10.6 & 11.8075 & -1.20752 \tabularnewline
15 & 12 & 12.9473 & -0.947302 \tabularnewline
16 & 6.3 & 12.327 & -6.02698 \tabularnewline
17 & 11.9 & 13.0628 & -1.16283 \tabularnewline
18 & 9.3 & 12.582 & -3.28201 \tabularnewline
19 & 10 & 11.9326 & -1.9326 \tabularnewline
20 & 6.4 & 13.0077 & -6.60769 \tabularnewline
21 & 13.8 & 13.082 & 0.718001 \tabularnewline
22 & 10.8 & 12.6634 & -1.86342 \tabularnewline
23 & 13.8 & 12.5283 & 1.27168 \tabularnewline
24 & 11.7 & 12.8295 & -1.12953 \tabularnewline
25 & 10.9 & 12.9112 & -2.01122 \tabularnewline
26 & 9.9 & 12.7416 & -2.84157 \tabularnewline
27 & 11.5 & 12.5615 & -1.06147 \tabularnewline
28 & 8.3 & 12.5897 & -4.28968 \tabularnewline
29 & 11.7 & 13.7452 & -2.04517 \tabularnewline
30 & 6.1 & 12.7206 & -6.62058 \tabularnewline
31 & 9 & 12.0823 & -3.08226 \tabularnewline
32 & 9.7 & 13.1885 & -3.48847 \tabularnewline
33 & 10.8 & 12.5563 & -1.75628 \tabularnewline
34 & 10.3 & 13.261 & -2.96096 \tabularnewline
35 & 10.4 & 13.2161 & -2.8161 \tabularnewline
36 & 9.3 & 13.2948 & -3.99483 \tabularnewline
37 & 11.8 & 12.6662 & -0.866192 \tabularnewline
38 & 5.9 & 14.0189 & -8.11894 \tabularnewline
39 & 11.4 & 13.8253 & -2.4253 \tabularnewline
40 & 13 & 13.0973 & -0.0973222 \tabularnewline
41 & 10.8 & 12.4119 & -1.61189 \tabularnewline
42 & 11.3 & 12.6634 & -1.36344 \tabularnewline
43 & 11.8 & 13.8109 & -2.01086 \tabularnewline
44 & 12.7 & 12.2772 & 0.422831 \tabularnewline
45 & 10.9 & 13.4683 & -2.56833 \tabularnewline
46 & 13.3 & 13.5031 & -0.20309 \tabularnewline
47 & 10.1 & 13.3608 & -3.2608 \tabularnewline
48 & 14.3 & 13.0272 & 1.27284 \tabularnewline
49 & 9.3 & 13.5945 & -4.29449 \tabularnewline
50 & 12.5 & 12.6779 & -0.177927 \tabularnewline
51 & 7.6 & 12.3932 & -4.79323 \tabularnewline
52 & 15.9 & 13.5994 & 2.3006 \tabularnewline
53 & 9.2 & 13.1465 & -3.9465 \tabularnewline
54 & 11.1 & 11.9272 & -0.827154 \tabularnewline
55 & 13 & 13.153 & -0.152985 \tabularnewline
56 & 14.5 & 13.1667 & 1.33331 \tabularnewline
57 & 12.3 & 13.239 & -0.939047 \tabularnewline
58 & 11.4 & 12.9562 & -1.55621 \tabularnewline
59 & 7.3 & 12.8535 & -5.55347 \tabularnewline
60 & 12.6 & 12.7058 & -0.105781 \tabularnewline
61 & 13 & 12.9422 & 0.0578376 \tabularnewline
62 & 13.2 & 13.115 & 0.0849968 \tabularnewline
63 & 7.7 & 12.5765 & -4.87649 \tabularnewline
64 & 4.35 & 13.8353 & -9.48533 \tabularnewline
65 & 12.7 & 13.3967 & -0.696666 \tabularnewline
66 & 18.1 & 13.3108 & 4.78925 \tabularnewline
67 & 17.85 & 12.3091 & 5.54093 \tabularnewline
68 & 17.1 & 13.902 & 3.19797 \tabularnewline
69 & 19.1 & 12.983 & 6.11705 \tabularnewline
70 & 16.1 & 12.7658 & 3.33417 \tabularnewline
71 & 13.35 & 13.4018 & -0.0518329 \tabularnewline
72 & 18.4 & 14.3127 & 4.08728 \tabularnewline
73 & 14.7 & 12.1589 & 2.54109 \tabularnewline
74 & 10.6 & 12.7068 & -2.10681 \tabularnewline
75 & 12.6 & 12.6867 & -0.086689 \tabularnewline
76 & 16.2 & 13.2448 & 2.95521 \tabularnewline
77 & 13.6 & 12.9705 & 0.629497 \tabularnewline
78 & 14.1 & 13.3983 & 0.701727 \tabularnewline
79 & 14.5 & 13.1501 & 1.34989 \tabularnewline
80 & 16.15 & 12.4861 & 3.66394 \tabularnewline
81 & 14.75 & 13.0594 & 1.6906 \tabularnewline
82 & 14.8 & 12.4575 & 2.34252 \tabularnewline
83 & 12.45 & 14.046 & -1.59601 \tabularnewline
84 & 12.65 & 12.6743 & -0.02425 \tabularnewline
85 & 17.35 & 13.1954 & 4.15463 \tabularnewline
86 & 8.6 & 13.279 & -4.67904 \tabularnewline
87 & 18.4 & 12.7924 & 5.60757 \tabularnewline
88 & 16.1 & 14.5517 & 1.54829 \tabularnewline
89 & 17.75 & 13.3303 & 4.41975 \tabularnewline
90 & 15.25 & 12.2833 & 2.9667 \tabularnewline
91 & 17.65 & 12.6836 & 4.9664 \tabularnewline
92 & 15.6 & 13.3489 & 2.25106 \tabularnewline
93 & 16.35 & 13.3372 & 3.01281 \tabularnewline
94 & 17.65 & 14.3045 & 3.34552 \tabularnewline
95 & 13.6 & 14.0952 & -0.495234 \tabularnewline
96 & 11.7 & 13.3814 & -1.68141 \tabularnewline
97 & 14.35 & 13.9507 & 0.399273 \tabularnewline
98 & 14.75 & 13.581 & 1.16896 \tabularnewline
99 & 18.25 & 13.5348 & 4.71519 \tabularnewline
100 & 9.9 & 12.6417 & -2.74168 \tabularnewline
101 & 16 & 13.4685 & 2.53148 \tabularnewline
102 & 18.25 & 13.4558 & 4.79416 \tabularnewline
103 & 16.85 & 13.5409 & 3.30913 \tabularnewline
104 & 18.95 & 13.1852 & 5.76483 \tabularnewline
105 & 15.6 & 13.4073 & 2.19268 \tabularnewline
106 & 17.1 & 13.673 & 3.42699 \tabularnewline
107 & 16.1 & 13.6293 & 2.47067 \tabularnewline
108 & 15.4 & 13.698 & 1.70197 \tabularnewline
109 & 15.4 & 13.4829 & 1.9171 \tabularnewline
110 & 13.35 & 14.0511 & -0.701076 \tabularnewline
111 & 19.1 & 13.4222 & 5.67777 \tabularnewline
112 & 7.6 & 12.719 & -5.11897 \tabularnewline
113 & 19.1 & 13.9842 & 5.11584 \tabularnewline
114 & 14.75 & 12.9327 & 1.81735 \tabularnewline
115 & 19.25 & 15.2395 & 4.0105 \tabularnewline
116 & 13.6 & 13.026 & 0.574013 \tabularnewline
117 & 12.75 & 11.7621 & 0.987892 \tabularnewline
118 & 9.85 & 13.163 & -3.313 \tabularnewline
119 & 15.25 & 13.4489 & 1.80108 \tabularnewline
120 & 11.9 & 12.3558 & -0.455804 \tabularnewline
121 & 16.35 & 12.6764 & 3.67362 \tabularnewline
122 & 12.4 & 13.694 & -1.29397 \tabularnewline
123 & 14.35 & 15.2763 & -0.926348 \tabularnewline
124 & 18.15 & 12.7545 & 5.39551 \tabularnewline
125 & 17.75 & 11.9297 & 5.82035 \tabularnewline
126 & 12.35 & 13.645 & -1.29497 \tabularnewline
127 & 15.6 & 12.929 & 2.67097 \tabularnewline
128 & 19.3 & 12.391 & 6.90898 \tabularnewline
129 & 17.1 & 13.069 & 4.03103 \tabularnewline
130 & 18.4 & 12.5129 & 5.88712 \tabularnewline
131 & 19.05 & 13.1202 & 5.9298 \tabularnewline
132 & 18.55 & 13.1865 & 5.36353 \tabularnewline
133 & 19.1 & 13.9278 & 5.17222 \tabularnewline
134 & 12.85 & 12.6754 & 0.174608 \tabularnewline
135 & 9.5 & 13.6869 & -4.18691 \tabularnewline
136 & 4.5 & 13.248 & -8.74799 \tabularnewline
137 & 13.6 & 12.6644 & 0.935582 \tabularnewline
138 & 11.7 & 15.1151 & -3.41513 \tabularnewline
139 & 13.35 & 13.1557 & 0.194321 \tabularnewline
140 & 17.75 & 13.6968 & 4.05321 \tabularnewline
141 & 17.6 & 13.0559 & 4.54415 \tabularnewline
142 & 14.05 & 13.28 & 0.769981 \tabularnewline
143 & 16.1 & 12.5134 & 3.5866 \tabularnewline
144 & 13.35 & 13.2274 & 0.122622 \tabularnewline
145 & 11.85 & 12.6279 & -0.77787 \tabularnewline
146 & 11.95 & 13.4828 & -1.53279 \tabularnewline
147 & 13.2 & 13.0997 & 0.100308 \tabularnewline
148 & 7.7 & 11.9902 & -4.29024 \tabularnewline
149 & 14.6 & 13.7315 & 0.868474 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280313&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]12.9[/C][C]14.1442[/C][C]-1.24419[/C][/ROW]
[ROW][C]2[/C][C]7.4[/C][C]12.2872[/C][C]-4.88718[/C][/ROW]
[ROW][C]3[/C][C]12.2[/C][C]12.6909[/C][C]-0.490945[/C][/ROW]
[ROW][C]4[/C][C]12.8[/C][C]13.861[/C][C]-1.06096[/C][/ROW]
[ROW][C]5[/C][C]7.4[/C][C]12.766[/C][C]-5.36602[/C][/ROW]
[ROW][C]6[/C][C]6.7[/C][C]13.7784[/C][C]-7.0784[/C][/ROW]
[ROW][C]7[/C][C]12.6[/C][C]12.589[/C][C]0.0109581[/C][/ROW]
[ROW][C]8[/C][C]14.8[/C][C]12.0316[/C][C]2.76842[/C][/ROW]
[ROW][C]9[/C][C]13.3[/C][C]14.6272[/C][C]-1.32718[/C][/ROW]
[ROW][C]10[/C][C]11.1[/C][C]11.2652[/C][C]-0.165241[/C][/ROW]
[ROW][C]11[/C][C]8.2[/C][C]14.4274[/C][C]-6.22745[/C][/ROW]
[ROW][C]12[/C][C]11.4[/C][C]12.5835[/C][C]-1.18352[/C][/ROW]
[ROW][C]13[/C][C]6.4[/C][C]12.8261[/C][C]-6.42614[/C][/ROW]
[ROW][C]14[/C][C]10.6[/C][C]11.8075[/C][C]-1.20752[/C][/ROW]
[ROW][C]15[/C][C]12[/C][C]12.9473[/C][C]-0.947302[/C][/ROW]
[ROW][C]16[/C][C]6.3[/C][C]12.327[/C][C]-6.02698[/C][/ROW]
[ROW][C]17[/C][C]11.9[/C][C]13.0628[/C][C]-1.16283[/C][/ROW]
[ROW][C]18[/C][C]9.3[/C][C]12.582[/C][C]-3.28201[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]11.9326[/C][C]-1.9326[/C][/ROW]
[ROW][C]20[/C][C]6.4[/C][C]13.0077[/C][C]-6.60769[/C][/ROW]
[ROW][C]21[/C][C]13.8[/C][C]13.082[/C][C]0.718001[/C][/ROW]
[ROW][C]22[/C][C]10.8[/C][C]12.6634[/C][C]-1.86342[/C][/ROW]
[ROW][C]23[/C][C]13.8[/C][C]12.5283[/C][C]1.27168[/C][/ROW]
[ROW][C]24[/C][C]11.7[/C][C]12.8295[/C][C]-1.12953[/C][/ROW]
[ROW][C]25[/C][C]10.9[/C][C]12.9112[/C][C]-2.01122[/C][/ROW]
[ROW][C]26[/C][C]9.9[/C][C]12.7416[/C][C]-2.84157[/C][/ROW]
[ROW][C]27[/C][C]11.5[/C][C]12.5615[/C][C]-1.06147[/C][/ROW]
[ROW][C]28[/C][C]8.3[/C][C]12.5897[/C][C]-4.28968[/C][/ROW]
[ROW][C]29[/C][C]11.7[/C][C]13.7452[/C][C]-2.04517[/C][/ROW]
[ROW][C]30[/C][C]6.1[/C][C]12.7206[/C][C]-6.62058[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]12.0823[/C][C]-3.08226[/C][/ROW]
[ROW][C]32[/C][C]9.7[/C][C]13.1885[/C][C]-3.48847[/C][/ROW]
[ROW][C]33[/C][C]10.8[/C][C]12.5563[/C][C]-1.75628[/C][/ROW]
[ROW][C]34[/C][C]10.3[/C][C]13.261[/C][C]-2.96096[/C][/ROW]
[ROW][C]35[/C][C]10.4[/C][C]13.2161[/C][C]-2.8161[/C][/ROW]
[ROW][C]36[/C][C]9.3[/C][C]13.2948[/C][C]-3.99483[/C][/ROW]
[ROW][C]37[/C][C]11.8[/C][C]12.6662[/C][C]-0.866192[/C][/ROW]
[ROW][C]38[/C][C]5.9[/C][C]14.0189[/C][C]-8.11894[/C][/ROW]
[ROW][C]39[/C][C]11.4[/C][C]13.8253[/C][C]-2.4253[/C][/ROW]
[ROW][C]40[/C][C]13[/C][C]13.0973[/C][C]-0.0973222[/C][/ROW]
[ROW][C]41[/C][C]10.8[/C][C]12.4119[/C][C]-1.61189[/C][/ROW]
[ROW][C]42[/C][C]11.3[/C][C]12.6634[/C][C]-1.36344[/C][/ROW]
[ROW][C]43[/C][C]11.8[/C][C]13.8109[/C][C]-2.01086[/C][/ROW]
[ROW][C]44[/C][C]12.7[/C][C]12.2772[/C][C]0.422831[/C][/ROW]
[ROW][C]45[/C][C]10.9[/C][C]13.4683[/C][C]-2.56833[/C][/ROW]
[ROW][C]46[/C][C]13.3[/C][C]13.5031[/C][C]-0.20309[/C][/ROW]
[ROW][C]47[/C][C]10.1[/C][C]13.3608[/C][C]-3.2608[/C][/ROW]
[ROW][C]48[/C][C]14.3[/C][C]13.0272[/C][C]1.27284[/C][/ROW]
[ROW][C]49[/C][C]9.3[/C][C]13.5945[/C][C]-4.29449[/C][/ROW]
[ROW][C]50[/C][C]12.5[/C][C]12.6779[/C][C]-0.177927[/C][/ROW]
[ROW][C]51[/C][C]7.6[/C][C]12.3932[/C][C]-4.79323[/C][/ROW]
[ROW][C]52[/C][C]15.9[/C][C]13.5994[/C][C]2.3006[/C][/ROW]
[ROW][C]53[/C][C]9.2[/C][C]13.1465[/C][C]-3.9465[/C][/ROW]
[ROW][C]54[/C][C]11.1[/C][C]11.9272[/C][C]-0.827154[/C][/ROW]
[ROW][C]55[/C][C]13[/C][C]13.153[/C][C]-0.152985[/C][/ROW]
[ROW][C]56[/C][C]14.5[/C][C]13.1667[/C][C]1.33331[/C][/ROW]
[ROW][C]57[/C][C]12.3[/C][C]13.239[/C][C]-0.939047[/C][/ROW]
[ROW][C]58[/C][C]11.4[/C][C]12.9562[/C][C]-1.55621[/C][/ROW]
[ROW][C]59[/C][C]7.3[/C][C]12.8535[/C][C]-5.55347[/C][/ROW]
[ROW][C]60[/C][C]12.6[/C][C]12.7058[/C][C]-0.105781[/C][/ROW]
[ROW][C]61[/C][C]13[/C][C]12.9422[/C][C]0.0578376[/C][/ROW]
[ROW][C]62[/C][C]13.2[/C][C]13.115[/C][C]0.0849968[/C][/ROW]
[ROW][C]63[/C][C]7.7[/C][C]12.5765[/C][C]-4.87649[/C][/ROW]
[ROW][C]64[/C][C]4.35[/C][C]13.8353[/C][C]-9.48533[/C][/ROW]
[ROW][C]65[/C][C]12.7[/C][C]13.3967[/C][C]-0.696666[/C][/ROW]
[ROW][C]66[/C][C]18.1[/C][C]13.3108[/C][C]4.78925[/C][/ROW]
[ROW][C]67[/C][C]17.85[/C][C]12.3091[/C][C]5.54093[/C][/ROW]
[ROW][C]68[/C][C]17.1[/C][C]13.902[/C][C]3.19797[/C][/ROW]
[ROW][C]69[/C][C]19.1[/C][C]12.983[/C][C]6.11705[/C][/ROW]
[ROW][C]70[/C][C]16.1[/C][C]12.7658[/C][C]3.33417[/C][/ROW]
[ROW][C]71[/C][C]13.35[/C][C]13.4018[/C][C]-0.0518329[/C][/ROW]
[ROW][C]72[/C][C]18.4[/C][C]14.3127[/C][C]4.08728[/C][/ROW]
[ROW][C]73[/C][C]14.7[/C][C]12.1589[/C][C]2.54109[/C][/ROW]
[ROW][C]74[/C][C]10.6[/C][C]12.7068[/C][C]-2.10681[/C][/ROW]
[ROW][C]75[/C][C]12.6[/C][C]12.6867[/C][C]-0.086689[/C][/ROW]
[ROW][C]76[/C][C]16.2[/C][C]13.2448[/C][C]2.95521[/C][/ROW]
[ROW][C]77[/C][C]13.6[/C][C]12.9705[/C][C]0.629497[/C][/ROW]
[ROW][C]78[/C][C]14.1[/C][C]13.3983[/C][C]0.701727[/C][/ROW]
[ROW][C]79[/C][C]14.5[/C][C]13.1501[/C][C]1.34989[/C][/ROW]
[ROW][C]80[/C][C]16.15[/C][C]12.4861[/C][C]3.66394[/C][/ROW]
[ROW][C]81[/C][C]14.75[/C][C]13.0594[/C][C]1.6906[/C][/ROW]
[ROW][C]82[/C][C]14.8[/C][C]12.4575[/C][C]2.34252[/C][/ROW]
[ROW][C]83[/C][C]12.45[/C][C]14.046[/C][C]-1.59601[/C][/ROW]
[ROW][C]84[/C][C]12.65[/C][C]12.6743[/C][C]-0.02425[/C][/ROW]
[ROW][C]85[/C][C]17.35[/C][C]13.1954[/C][C]4.15463[/C][/ROW]
[ROW][C]86[/C][C]8.6[/C][C]13.279[/C][C]-4.67904[/C][/ROW]
[ROW][C]87[/C][C]18.4[/C][C]12.7924[/C][C]5.60757[/C][/ROW]
[ROW][C]88[/C][C]16.1[/C][C]14.5517[/C][C]1.54829[/C][/ROW]
[ROW][C]89[/C][C]17.75[/C][C]13.3303[/C][C]4.41975[/C][/ROW]
[ROW][C]90[/C][C]15.25[/C][C]12.2833[/C][C]2.9667[/C][/ROW]
[ROW][C]91[/C][C]17.65[/C][C]12.6836[/C][C]4.9664[/C][/ROW]
[ROW][C]92[/C][C]15.6[/C][C]13.3489[/C][C]2.25106[/C][/ROW]
[ROW][C]93[/C][C]16.35[/C][C]13.3372[/C][C]3.01281[/C][/ROW]
[ROW][C]94[/C][C]17.65[/C][C]14.3045[/C][C]3.34552[/C][/ROW]
[ROW][C]95[/C][C]13.6[/C][C]14.0952[/C][C]-0.495234[/C][/ROW]
[ROW][C]96[/C][C]11.7[/C][C]13.3814[/C][C]-1.68141[/C][/ROW]
[ROW][C]97[/C][C]14.35[/C][C]13.9507[/C][C]0.399273[/C][/ROW]
[ROW][C]98[/C][C]14.75[/C][C]13.581[/C][C]1.16896[/C][/ROW]
[ROW][C]99[/C][C]18.25[/C][C]13.5348[/C][C]4.71519[/C][/ROW]
[ROW][C]100[/C][C]9.9[/C][C]12.6417[/C][C]-2.74168[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]13.4685[/C][C]2.53148[/C][/ROW]
[ROW][C]102[/C][C]18.25[/C][C]13.4558[/C][C]4.79416[/C][/ROW]
[ROW][C]103[/C][C]16.85[/C][C]13.5409[/C][C]3.30913[/C][/ROW]
[ROW][C]104[/C][C]18.95[/C][C]13.1852[/C][C]5.76483[/C][/ROW]
[ROW][C]105[/C][C]15.6[/C][C]13.4073[/C][C]2.19268[/C][/ROW]
[ROW][C]106[/C][C]17.1[/C][C]13.673[/C][C]3.42699[/C][/ROW]
[ROW][C]107[/C][C]16.1[/C][C]13.6293[/C][C]2.47067[/C][/ROW]
[ROW][C]108[/C][C]15.4[/C][C]13.698[/C][C]1.70197[/C][/ROW]
[ROW][C]109[/C][C]15.4[/C][C]13.4829[/C][C]1.9171[/C][/ROW]
[ROW][C]110[/C][C]13.35[/C][C]14.0511[/C][C]-0.701076[/C][/ROW]
[ROW][C]111[/C][C]19.1[/C][C]13.4222[/C][C]5.67777[/C][/ROW]
[ROW][C]112[/C][C]7.6[/C][C]12.719[/C][C]-5.11897[/C][/ROW]
[ROW][C]113[/C][C]19.1[/C][C]13.9842[/C][C]5.11584[/C][/ROW]
[ROW][C]114[/C][C]14.75[/C][C]12.9327[/C][C]1.81735[/C][/ROW]
[ROW][C]115[/C][C]19.25[/C][C]15.2395[/C][C]4.0105[/C][/ROW]
[ROW][C]116[/C][C]13.6[/C][C]13.026[/C][C]0.574013[/C][/ROW]
[ROW][C]117[/C][C]12.75[/C][C]11.7621[/C][C]0.987892[/C][/ROW]
[ROW][C]118[/C][C]9.85[/C][C]13.163[/C][C]-3.313[/C][/ROW]
[ROW][C]119[/C][C]15.25[/C][C]13.4489[/C][C]1.80108[/C][/ROW]
[ROW][C]120[/C][C]11.9[/C][C]12.3558[/C][C]-0.455804[/C][/ROW]
[ROW][C]121[/C][C]16.35[/C][C]12.6764[/C][C]3.67362[/C][/ROW]
[ROW][C]122[/C][C]12.4[/C][C]13.694[/C][C]-1.29397[/C][/ROW]
[ROW][C]123[/C][C]14.35[/C][C]15.2763[/C][C]-0.926348[/C][/ROW]
[ROW][C]124[/C][C]18.15[/C][C]12.7545[/C][C]5.39551[/C][/ROW]
[ROW][C]125[/C][C]17.75[/C][C]11.9297[/C][C]5.82035[/C][/ROW]
[ROW][C]126[/C][C]12.35[/C][C]13.645[/C][C]-1.29497[/C][/ROW]
[ROW][C]127[/C][C]15.6[/C][C]12.929[/C][C]2.67097[/C][/ROW]
[ROW][C]128[/C][C]19.3[/C][C]12.391[/C][C]6.90898[/C][/ROW]
[ROW][C]129[/C][C]17.1[/C][C]13.069[/C][C]4.03103[/C][/ROW]
[ROW][C]130[/C][C]18.4[/C][C]12.5129[/C][C]5.88712[/C][/ROW]
[ROW][C]131[/C][C]19.05[/C][C]13.1202[/C][C]5.9298[/C][/ROW]
[ROW][C]132[/C][C]18.55[/C][C]13.1865[/C][C]5.36353[/C][/ROW]
[ROW][C]133[/C][C]19.1[/C][C]13.9278[/C][C]5.17222[/C][/ROW]
[ROW][C]134[/C][C]12.85[/C][C]12.6754[/C][C]0.174608[/C][/ROW]
[ROW][C]135[/C][C]9.5[/C][C]13.6869[/C][C]-4.18691[/C][/ROW]
[ROW][C]136[/C][C]4.5[/C][C]13.248[/C][C]-8.74799[/C][/ROW]
[ROW][C]137[/C][C]13.6[/C][C]12.6644[/C][C]0.935582[/C][/ROW]
[ROW][C]138[/C][C]11.7[/C][C]15.1151[/C][C]-3.41513[/C][/ROW]
[ROW][C]139[/C][C]13.35[/C][C]13.1557[/C][C]0.194321[/C][/ROW]
[ROW][C]140[/C][C]17.75[/C][C]13.6968[/C][C]4.05321[/C][/ROW]
[ROW][C]141[/C][C]17.6[/C][C]13.0559[/C][C]4.54415[/C][/ROW]
[ROW][C]142[/C][C]14.05[/C][C]13.28[/C][C]0.769981[/C][/ROW]
[ROW][C]143[/C][C]16.1[/C][C]12.5134[/C][C]3.5866[/C][/ROW]
[ROW][C]144[/C][C]13.35[/C][C]13.2274[/C][C]0.122622[/C][/ROW]
[ROW][C]145[/C][C]11.85[/C][C]12.6279[/C][C]-0.77787[/C][/ROW]
[ROW][C]146[/C][C]11.95[/C][C]13.4828[/C][C]-1.53279[/C][/ROW]
[ROW][C]147[/C][C]13.2[/C][C]13.0997[/C][C]0.100308[/C][/ROW]
[ROW][C]148[/C][C]7.7[/C][C]11.9902[/C][C]-4.29024[/C][/ROW]
[ROW][C]149[/C][C]14.6[/C][C]13.7315[/C][C]0.868474[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280313&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280313&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
112.914.1442-1.24419
27.412.2872-4.88718
312.212.6909-0.490945
412.813.861-1.06096
57.412.766-5.36602
66.713.7784-7.0784
712.612.5890.0109581
814.812.03162.76842
913.314.6272-1.32718
1011.111.2652-0.165241
118.214.4274-6.22745
1211.412.5835-1.18352
136.412.8261-6.42614
1410.611.8075-1.20752
151212.9473-0.947302
166.312.327-6.02698
1711.913.0628-1.16283
189.312.582-3.28201
191011.9326-1.9326
206.413.0077-6.60769
2113.813.0820.718001
2210.812.6634-1.86342
2313.812.52831.27168
2411.712.8295-1.12953
2510.912.9112-2.01122
269.912.7416-2.84157
2711.512.5615-1.06147
288.312.5897-4.28968
2911.713.7452-2.04517
306.112.7206-6.62058
31912.0823-3.08226
329.713.1885-3.48847
3310.812.5563-1.75628
3410.313.261-2.96096
3510.413.2161-2.8161
369.313.2948-3.99483
3711.812.6662-0.866192
385.914.0189-8.11894
3911.413.8253-2.4253
401313.0973-0.0973222
4110.812.4119-1.61189
4211.312.6634-1.36344
4311.813.8109-2.01086
4412.712.27720.422831
4510.913.4683-2.56833
4613.313.5031-0.20309
4710.113.3608-3.2608
4814.313.02721.27284
499.313.5945-4.29449
5012.512.6779-0.177927
517.612.3932-4.79323
5215.913.59942.3006
539.213.1465-3.9465
5411.111.9272-0.827154
551313.153-0.152985
5614.513.16671.33331
5712.313.239-0.939047
5811.412.9562-1.55621
597.312.8535-5.55347
6012.612.7058-0.105781
611312.94220.0578376
6213.213.1150.0849968
637.712.5765-4.87649
644.3513.8353-9.48533
6512.713.3967-0.696666
6618.113.31084.78925
6717.8512.30915.54093
6817.113.9023.19797
6919.112.9836.11705
7016.112.76583.33417
7113.3513.4018-0.0518329
7218.414.31274.08728
7314.712.15892.54109
7410.612.7068-2.10681
7512.612.6867-0.086689
7616.213.24482.95521
7713.612.97050.629497
7814.113.39830.701727
7914.513.15011.34989
8016.1512.48613.66394
8114.7513.05941.6906
8214.812.45752.34252
8312.4514.046-1.59601
8412.6512.6743-0.02425
8517.3513.19544.15463
868.613.279-4.67904
8718.412.79245.60757
8816.114.55171.54829
8917.7513.33034.41975
9015.2512.28332.9667
9117.6512.68364.9664
9215.613.34892.25106
9316.3513.33723.01281
9417.6514.30453.34552
9513.614.0952-0.495234
9611.713.3814-1.68141
9714.3513.95070.399273
9814.7513.5811.16896
9918.2513.53484.71519
1009.912.6417-2.74168
1011613.46852.53148
10218.2513.45584.79416
10316.8513.54093.30913
10418.9513.18525.76483
10515.613.40732.19268
10617.113.6733.42699
10716.113.62932.47067
10815.413.6981.70197
10915.413.48291.9171
11013.3514.0511-0.701076
11119.113.42225.67777
1127.612.719-5.11897
11319.113.98425.11584
11414.7512.93271.81735
11519.2515.23954.0105
11613.613.0260.574013
11712.7511.76210.987892
1189.8513.163-3.313
11915.2513.44891.80108
12011.912.3558-0.455804
12116.3512.67643.67362
12212.413.694-1.29397
12314.3515.2763-0.926348
12418.1512.75455.39551
12517.7511.92975.82035
12612.3513.645-1.29497
12715.612.9292.67097
12819.312.3916.90898
12917.113.0694.03103
13018.412.51295.88712
13119.0513.12025.9298
13218.5513.18655.36353
13319.113.92785.17222
13412.8512.67540.174608
1359.513.6869-4.18691
1364.513.248-8.74799
13713.612.66440.935582
13811.715.1151-3.41513
13913.3513.15570.194321
14017.7513.69684.05321
14117.613.05594.54415
14214.0513.280.769981
14316.112.51343.5866
14413.3513.22740.122622
14511.8512.6279-0.77787
14611.9513.4828-1.53279
14713.213.09970.100308
1487.711.9902-4.29024
14914.613.73150.868474






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.3632080.7264160.636792
110.2617990.5235980.738201
120.167060.334120.83294
130.3538710.7077420.646129
140.2491540.4983080.750846
150.169180.3383590.83082
160.184730.3694610.81527
170.1265190.2530380.873481
180.09664650.1932930.903354
190.06795960.1359190.93204
200.04966190.09932370.950338
210.03061050.06122090.96939
220.01886830.03773660.981132
230.01570460.03140920.984295
240.01241370.02482740.987586
250.008380190.01676040.99162
260.005109210.01021840.994891
270.00304350.006087010.996956
280.002513930.005027860.997486
290.001538890.003077790.998461
300.001198060.002396130.998802
310.0009725290.001945060.999027
320.02131530.04263060.978685
330.01447140.02894290.985529
340.01168370.02336730.988316
350.008199450.01639890.991801
360.006327860.01265570.993672
370.003999840.007999670.996
380.01231710.02463420.987683
390.009549560.01909910.99045
400.00901620.01803240.990984
410.009545470.01909090.990455
420.006975590.01395120.993024
430.005713410.01142680.994287
440.004379010.008758020.995621
450.003311130.006622250.996689
460.002935550.005871110.997064
470.002491380.004982770.997509
480.002531840.005063670.997468
490.002367470.004734930.997633
500.002035910.004071820.997964
510.002846020.005692040.997154
520.005167230.01033450.994833
530.005404040.01080810.994596
540.00415510.00831020.995845
550.003807670.007615340.996192
560.003786860.007573710.996213
570.002673230.005346470.997327
580.002004430.004008850.997996
590.00346420.006928410.996536
600.002398360.004796710.997602
610.002377250.004754490.997623
620.002167610.004335210.997832
630.003782510.007565010.996217
640.03782340.07564680.962177
650.04198850.08397710.958011
660.1106950.221390.889305
670.2153250.4306510.784675
680.297860.595720.70214
690.4639630.9279260.536037
700.4980730.9961450.501927
710.472510.9450210.52749
720.5432950.913410.456705
730.5275260.9449470.472474
740.537730.9245390.46227
750.5041640.9916710.495836
760.4989250.997850.501075
770.4621640.9243270.537836
780.4301760.8603520.569824
790.402430.8048610.59757
800.4058430.8116850.594157
810.3758240.7516480.624176
820.3621320.7242650.637868
830.3509890.7019780.649011
840.3151230.6302460.684877
850.3302480.6604950.669752
860.3778980.7557950.622102
870.4542950.9085890.545705
880.4406120.8812250.559388
890.4746810.9493610.525319
900.4552290.9104590.544771
910.5098440.9803130.490156
920.4826950.9653890.517305
930.4685410.9370810.531459
940.4700610.9401220.529939
950.4272490.8544980.572751
960.4234480.8468970.576552
970.3770030.7540070.622997
980.3415290.6830590.658471
990.355830.7116610.64417
1000.3467610.6935210.653239
1010.3180440.6360880.681956
1020.3383970.6767930.661603
1030.3199740.6399480.680026
1040.3660580.7321160.633942
1050.3284430.6568850.671557
1060.3034910.6069820.696509
1070.2720150.5440290.727985
1080.2407290.4814580.759271
1090.2071550.4143090.792845
1100.1712450.3424890.828755
1110.189740.379480.81026
1120.2726380.5452760.727362
1130.3775840.7551670.622416
1140.3270370.6540740.672963
1150.3477020.6954040.652298
1160.2991290.5982570.700871
1170.2650110.5300210.734989
1180.3463210.6926430.653679
1190.3037880.6075750.696212
1200.3158180.6316360.684182
1210.273890.5477810.72611
1220.2242150.4484290.775785
1230.1872050.374410.812795
1240.1856860.3713720.814314
1250.1714710.3429420.828529
1260.1309850.261970.869015
1270.1010740.2021480.898926
1280.1928370.3856730.807163
1290.160380.3207610.83962
1300.1913090.3826180.808691
1310.2682890.5365790.731711
1320.491170.9823390.50883
1330.4150250.830050.584975
1340.3212210.6424430.678779
1350.4480940.8961880.551906
1360.7663190.4673630.233681
1370.6475270.7049470.352473
1380.9454210.1091590.0545793
1390.8621920.2756150.137808

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.363208 & 0.726416 & 0.636792 \tabularnewline
11 & 0.261799 & 0.523598 & 0.738201 \tabularnewline
12 & 0.16706 & 0.33412 & 0.83294 \tabularnewline
13 & 0.353871 & 0.707742 & 0.646129 \tabularnewline
14 & 0.249154 & 0.498308 & 0.750846 \tabularnewline
15 & 0.16918 & 0.338359 & 0.83082 \tabularnewline
16 & 0.18473 & 0.369461 & 0.81527 \tabularnewline
17 & 0.126519 & 0.253038 & 0.873481 \tabularnewline
18 & 0.0966465 & 0.193293 & 0.903354 \tabularnewline
19 & 0.0679596 & 0.135919 & 0.93204 \tabularnewline
20 & 0.0496619 & 0.0993237 & 0.950338 \tabularnewline
21 & 0.0306105 & 0.0612209 & 0.96939 \tabularnewline
22 & 0.0188683 & 0.0377366 & 0.981132 \tabularnewline
23 & 0.0157046 & 0.0314092 & 0.984295 \tabularnewline
24 & 0.0124137 & 0.0248274 & 0.987586 \tabularnewline
25 & 0.00838019 & 0.0167604 & 0.99162 \tabularnewline
26 & 0.00510921 & 0.0102184 & 0.994891 \tabularnewline
27 & 0.0030435 & 0.00608701 & 0.996956 \tabularnewline
28 & 0.00251393 & 0.00502786 & 0.997486 \tabularnewline
29 & 0.00153889 & 0.00307779 & 0.998461 \tabularnewline
30 & 0.00119806 & 0.00239613 & 0.998802 \tabularnewline
31 & 0.000972529 & 0.00194506 & 0.999027 \tabularnewline
32 & 0.0213153 & 0.0426306 & 0.978685 \tabularnewline
33 & 0.0144714 & 0.0289429 & 0.985529 \tabularnewline
34 & 0.0116837 & 0.0233673 & 0.988316 \tabularnewline
35 & 0.00819945 & 0.0163989 & 0.991801 \tabularnewline
36 & 0.00632786 & 0.0126557 & 0.993672 \tabularnewline
37 & 0.00399984 & 0.00799967 & 0.996 \tabularnewline
38 & 0.0123171 & 0.0246342 & 0.987683 \tabularnewline
39 & 0.00954956 & 0.0190991 & 0.99045 \tabularnewline
40 & 0.0090162 & 0.0180324 & 0.990984 \tabularnewline
41 & 0.00954547 & 0.0190909 & 0.990455 \tabularnewline
42 & 0.00697559 & 0.0139512 & 0.993024 \tabularnewline
43 & 0.00571341 & 0.0114268 & 0.994287 \tabularnewline
44 & 0.00437901 & 0.00875802 & 0.995621 \tabularnewline
45 & 0.00331113 & 0.00662225 & 0.996689 \tabularnewline
46 & 0.00293555 & 0.00587111 & 0.997064 \tabularnewline
47 & 0.00249138 & 0.00498277 & 0.997509 \tabularnewline
48 & 0.00253184 & 0.00506367 & 0.997468 \tabularnewline
49 & 0.00236747 & 0.00473493 & 0.997633 \tabularnewline
50 & 0.00203591 & 0.00407182 & 0.997964 \tabularnewline
51 & 0.00284602 & 0.00569204 & 0.997154 \tabularnewline
52 & 0.00516723 & 0.0103345 & 0.994833 \tabularnewline
53 & 0.00540404 & 0.0108081 & 0.994596 \tabularnewline
54 & 0.0041551 & 0.0083102 & 0.995845 \tabularnewline
55 & 0.00380767 & 0.00761534 & 0.996192 \tabularnewline
56 & 0.00378686 & 0.00757371 & 0.996213 \tabularnewline
57 & 0.00267323 & 0.00534647 & 0.997327 \tabularnewline
58 & 0.00200443 & 0.00400885 & 0.997996 \tabularnewline
59 & 0.0034642 & 0.00692841 & 0.996536 \tabularnewline
60 & 0.00239836 & 0.00479671 & 0.997602 \tabularnewline
61 & 0.00237725 & 0.00475449 & 0.997623 \tabularnewline
62 & 0.00216761 & 0.00433521 & 0.997832 \tabularnewline
63 & 0.00378251 & 0.00756501 & 0.996217 \tabularnewline
64 & 0.0378234 & 0.0756468 & 0.962177 \tabularnewline
65 & 0.0419885 & 0.0839771 & 0.958011 \tabularnewline
66 & 0.110695 & 0.22139 & 0.889305 \tabularnewline
67 & 0.215325 & 0.430651 & 0.784675 \tabularnewline
68 & 0.29786 & 0.59572 & 0.70214 \tabularnewline
69 & 0.463963 & 0.927926 & 0.536037 \tabularnewline
70 & 0.498073 & 0.996145 & 0.501927 \tabularnewline
71 & 0.47251 & 0.945021 & 0.52749 \tabularnewline
72 & 0.543295 & 0.91341 & 0.456705 \tabularnewline
73 & 0.527526 & 0.944947 & 0.472474 \tabularnewline
74 & 0.53773 & 0.924539 & 0.46227 \tabularnewline
75 & 0.504164 & 0.991671 & 0.495836 \tabularnewline
76 & 0.498925 & 0.99785 & 0.501075 \tabularnewline
77 & 0.462164 & 0.924327 & 0.537836 \tabularnewline
78 & 0.430176 & 0.860352 & 0.569824 \tabularnewline
79 & 0.40243 & 0.804861 & 0.59757 \tabularnewline
80 & 0.405843 & 0.811685 & 0.594157 \tabularnewline
81 & 0.375824 & 0.751648 & 0.624176 \tabularnewline
82 & 0.362132 & 0.724265 & 0.637868 \tabularnewline
83 & 0.350989 & 0.701978 & 0.649011 \tabularnewline
84 & 0.315123 & 0.630246 & 0.684877 \tabularnewline
85 & 0.330248 & 0.660495 & 0.669752 \tabularnewline
86 & 0.377898 & 0.755795 & 0.622102 \tabularnewline
87 & 0.454295 & 0.908589 & 0.545705 \tabularnewline
88 & 0.440612 & 0.881225 & 0.559388 \tabularnewline
89 & 0.474681 & 0.949361 & 0.525319 \tabularnewline
90 & 0.455229 & 0.910459 & 0.544771 \tabularnewline
91 & 0.509844 & 0.980313 & 0.490156 \tabularnewline
92 & 0.482695 & 0.965389 & 0.517305 \tabularnewline
93 & 0.468541 & 0.937081 & 0.531459 \tabularnewline
94 & 0.470061 & 0.940122 & 0.529939 \tabularnewline
95 & 0.427249 & 0.854498 & 0.572751 \tabularnewline
96 & 0.423448 & 0.846897 & 0.576552 \tabularnewline
97 & 0.377003 & 0.754007 & 0.622997 \tabularnewline
98 & 0.341529 & 0.683059 & 0.658471 \tabularnewline
99 & 0.35583 & 0.711661 & 0.64417 \tabularnewline
100 & 0.346761 & 0.693521 & 0.653239 \tabularnewline
101 & 0.318044 & 0.636088 & 0.681956 \tabularnewline
102 & 0.338397 & 0.676793 & 0.661603 \tabularnewline
103 & 0.319974 & 0.639948 & 0.680026 \tabularnewline
104 & 0.366058 & 0.732116 & 0.633942 \tabularnewline
105 & 0.328443 & 0.656885 & 0.671557 \tabularnewline
106 & 0.303491 & 0.606982 & 0.696509 \tabularnewline
107 & 0.272015 & 0.544029 & 0.727985 \tabularnewline
108 & 0.240729 & 0.481458 & 0.759271 \tabularnewline
109 & 0.207155 & 0.414309 & 0.792845 \tabularnewline
110 & 0.171245 & 0.342489 & 0.828755 \tabularnewline
111 & 0.18974 & 0.37948 & 0.81026 \tabularnewline
112 & 0.272638 & 0.545276 & 0.727362 \tabularnewline
113 & 0.377584 & 0.755167 & 0.622416 \tabularnewline
114 & 0.327037 & 0.654074 & 0.672963 \tabularnewline
115 & 0.347702 & 0.695404 & 0.652298 \tabularnewline
116 & 0.299129 & 0.598257 & 0.700871 \tabularnewline
117 & 0.265011 & 0.530021 & 0.734989 \tabularnewline
118 & 0.346321 & 0.692643 & 0.653679 \tabularnewline
119 & 0.303788 & 0.607575 & 0.696212 \tabularnewline
120 & 0.315818 & 0.631636 & 0.684182 \tabularnewline
121 & 0.27389 & 0.547781 & 0.72611 \tabularnewline
122 & 0.224215 & 0.448429 & 0.775785 \tabularnewline
123 & 0.187205 & 0.37441 & 0.812795 \tabularnewline
124 & 0.185686 & 0.371372 & 0.814314 \tabularnewline
125 & 0.171471 & 0.342942 & 0.828529 \tabularnewline
126 & 0.130985 & 0.26197 & 0.869015 \tabularnewline
127 & 0.101074 & 0.202148 & 0.898926 \tabularnewline
128 & 0.192837 & 0.385673 & 0.807163 \tabularnewline
129 & 0.16038 & 0.320761 & 0.83962 \tabularnewline
130 & 0.191309 & 0.382618 & 0.808691 \tabularnewline
131 & 0.268289 & 0.536579 & 0.731711 \tabularnewline
132 & 0.49117 & 0.982339 & 0.50883 \tabularnewline
133 & 0.415025 & 0.83005 & 0.584975 \tabularnewline
134 & 0.321221 & 0.642443 & 0.678779 \tabularnewline
135 & 0.448094 & 0.896188 & 0.551906 \tabularnewline
136 & 0.766319 & 0.467363 & 0.233681 \tabularnewline
137 & 0.647527 & 0.704947 & 0.352473 \tabularnewline
138 & 0.945421 & 0.109159 & 0.0545793 \tabularnewline
139 & 0.862192 & 0.275615 & 0.137808 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280313&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]0.363208[/C][C]0.726416[/C][C]0.636792[/C][/ROW]
[ROW][C]11[/C][C]0.261799[/C][C]0.523598[/C][C]0.738201[/C][/ROW]
[ROW][C]12[/C][C]0.16706[/C][C]0.33412[/C][C]0.83294[/C][/ROW]
[ROW][C]13[/C][C]0.353871[/C][C]0.707742[/C][C]0.646129[/C][/ROW]
[ROW][C]14[/C][C]0.249154[/C][C]0.498308[/C][C]0.750846[/C][/ROW]
[ROW][C]15[/C][C]0.16918[/C][C]0.338359[/C][C]0.83082[/C][/ROW]
[ROW][C]16[/C][C]0.18473[/C][C]0.369461[/C][C]0.81527[/C][/ROW]
[ROW][C]17[/C][C]0.126519[/C][C]0.253038[/C][C]0.873481[/C][/ROW]
[ROW][C]18[/C][C]0.0966465[/C][C]0.193293[/C][C]0.903354[/C][/ROW]
[ROW][C]19[/C][C]0.0679596[/C][C]0.135919[/C][C]0.93204[/C][/ROW]
[ROW][C]20[/C][C]0.0496619[/C][C]0.0993237[/C][C]0.950338[/C][/ROW]
[ROW][C]21[/C][C]0.0306105[/C][C]0.0612209[/C][C]0.96939[/C][/ROW]
[ROW][C]22[/C][C]0.0188683[/C][C]0.0377366[/C][C]0.981132[/C][/ROW]
[ROW][C]23[/C][C]0.0157046[/C][C]0.0314092[/C][C]0.984295[/C][/ROW]
[ROW][C]24[/C][C]0.0124137[/C][C]0.0248274[/C][C]0.987586[/C][/ROW]
[ROW][C]25[/C][C]0.00838019[/C][C]0.0167604[/C][C]0.99162[/C][/ROW]
[ROW][C]26[/C][C]0.00510921[/C][C]0.0102184[/C][C]0.994891[/C][/ROW]
[ROW][C]27[/C][C]0.0030435[/C][C]0.00608701[/C][C]0.996956[/C][/ROW]
[ROW][C]28[/C][C]0.00251393[/C][C]0.00502786[/C][C]0.997486[/C][/ROW]
[ROW][C]29[/C][C]0.00153889[/C][C]0.00307779[/C][C]0.998461[/C][/ROW]
[ROW][C]30[/C][C]0.00119806[/C][C]0.00239613[/C][C]0.998802[/C][/ROW]
[ROW][C]31[/C][C]0.000972529[/C][C]0.00194506[/C][C]0.999027[/C][/ROW]
[ROW][C]32[/C][C]0.0213153[/C][C]0.0426306[/C][C]0.978685[/C][/ROW]
[ROW][C]33[/C][C]0.0144714[/C][C]0.0289429[/C][C]0.985529[/C][/ROW]
[ROW][C]34[/C][C]0.0116837[/C][C]0.0233673[/C][C]0.988316[/C][/ROW]
[ROW][C]35[/C][C]0.00819945[/C][C]0.0163989[/C][C]0.991801[/C][/ROW]
[ROW][C]36[/C][C]0.00632786[/C][C]0.0126557[/C][C]0.993672[/C][/ROW]
[ROW][C]37[/C][C]0.00399984[/C][C]0.00799967[/C][C]0.996[/C][/ROW]
[ROW][C]38[/C][C]0.0123171[/C][C]0.0246342[/C][C]0.987683[/C][/ROW]
[ROW][C]39[/C][C]0.00954956[/C][C]0.0190991[/C][C]0.99045[/C][/ROW]
[ROW][C]40[/C][C]0.0090162[/C][C]0.0180324[/C][C]0.990984[/C][/ROW]
[ROW][C]41[/C][C]0.00954547[/C][C]0.0190909[/C][C]0.990455[/C][/ROW]
[ROW][C]42[/C][C]0.00697559[/C][C]0.0139512[/C][C]0.993024[/C][/ROW]
[ROW][C]43[/C][C]0.00571341[/C][C]0.0114268[/C][C]0.994287[/C][/ROW]
[ROW][C]44[/C][C]0.00437901[/C][C]0.00875802[/C][C]0.995621[/C][/ROW]
[ROW][C]45[/C][C]0.00331113[/C][C]0.00662225[/C][C]0.996689[/C][/ROW]
[ROW][C]46[/C][C]0.00293555[/C][C]0.00587111[/C][C]0.997064[/C][/ROW]
[ROW][C]47[/C][C]0.00249138[/C][C]0.00498277[/C][C]0.997509[/C][/ROW]
[ROW][C]48[/C][C]0.00253184[/C][C]0.00506367[/C][C]0.997468[/C][/ROW]
[ROW][C]49[/C][C]0.00236747[/C][C]0.00473493[/C][C]0.997633[/C][/ROW]
[ROW][C]50[/C][C]0.00203591[/C][C]0.00407182[/C][C]0.997964[/C][/ROW]
[ROW][C]51[/C][C]0.00284602[/C][C]0.00569204[/C][C]0.997154[/C][/ROW]
[ROW][C]52[/C][C]0.00516723[/C][C]0.0103345[/C][C]0.994833[/C][/ROW]
[ROW][C]53[/C][C]0.00540404[/C][C]0.0108081[/C][C]0.994596[/C][/ROW]
[ROW][C]54[/C][C]0.0041551[/C][C]0.0083102[/C][C]0.995845[/C][/ROW]
[ROW][C]55[/C][C]0.00380767[/C][C]0.00761534[/C][C]0.996192[/C][/ROW]
[ROW][C]56[/C][C]0.00378686[/C][C]0.00757371[/C][C]0.996213[/C][/ROW]
[ROW][C]57[/C][C]0.00267323[/C][C]0.00534647[/C][C]0.997327[/C][/ROW]
[ROW][C]58[/C][C]0.00200443[/C][C]0.00400885[/C][C]0.997996[/C][/ROW]
[ROW][C]59[/C][C]0.0034642[/C][C]0.00692841[/C][C]0.996536[/C][/ROW]
[ROW][C]60[/C][C]0.00239836[/C][C]0.00479671[/C][C]0.997602[/C][/ROW]
[ROW][C]61[/C][C]0.00237725[/C][C]0.00475449[/C][C]0.997623[/C][/ROW]
[ROW][C]62[/C][C]0.00216761[/C][C]0.00433521[/C][C]0.997832[/C][/ROW]
[ROW][C]63[/C][C]0.00378251[/C][C]0.00756501[/C][C]0.996217[/C][/ROW]
[ROW][C]64[/C][C]0.0378234[/C][C]0.0756468[/C][C]0.962177[/C][/ROW]
[ROW][C]65[/C][C]0.0419885[/C][C]0.0839771[/C][C]0.958011[/C][/ROW]
[ROW][C]66[/C][C]0.110695[/C][C]0.22139[/C][C]0.889305[/C][/ROW]
[ROW][C]67[/C][C]0.215325[/C][C]0.430651[/C][C]0.784675[/C][/ROW]
[ROW][C]68[/C][C]0.29786[/C][C]0.59572[/C][C]0.70214[/C][/ROW]
[ROW][C]69[/C][C]0.463963[/C][C]0.927926[/C][C]0.536037[/C][/ROW]
[ROW][C]70[/C][C]0.498073[/C][C]0.996145[/C][C]0.501927[/C][/ROW]
[ROW][C]71[/C][C]0.47251[/C][C]0.945021[/C][C]0.52749[/C][/ROW]
[ROW][C]72[/C][C]0.543295[/C][C]0.91341[/C][C]0.456705[/C][/ROW]
[ROW][C]73[/C][C]0.527526[/C][C]0.944947[/C][C]0.472474[/C][/ROW]
[ROW][C]74[/C][C]0.53773[/C][C]0.924539[/C][C]0.46227[/C][/ROW]
[ROW][C]75[/C][C]0.504164[/C][C]0.991671[/C][C]0.495836[/C][/ROW]
[ROW][C]76[/C][C]0.498925[/C][C]0.99785[/C][C]0.501075[/C][/ROW]
[ROW][C]77[/C][C]0.462164[/C][C]0.924327[/C][C]0.537836[/C][/ROW]
[ROW][C]78[/C][C]0.430176[/C][C]0.860352[/C][C]0.569824[/C][/ROW]
[ROW][C]79[/C][C]0.40243[/C][C]0.804861[/C][C]0.59757[/C][/ROW]
[ROW][C]80[/C][C]0.405843[/C][C]0.811685[/C][C]0.594157[/C][/ROW]
[ROW][C]81[/C][C]0.375824[/C][C]0.751648[/C][C]0.624176[/C][/ROW]
[ROW][C]82[/C][C]0.362132[/C][C]0.724265[/C][C]0.637868[/C][/ROW]
[ROW][C]83[/C][C]0.350989[/C][C]0.701978[/C][C]0.649011[/C][/ROW]
[ROW][C]84[/C][C]0.315123[/C][C]0.630246[/C][C]0.684877[/C][/ROW]
[ROW][C]85[/C][C]0.330248[/C][C]0.660495[/C][C]0.669752[/C][/ROW]
[ROW][C]86[/C][C]0.377898[/C][C]0.755795[/C][C]0.622102[/C][/ROW]
[ROW][C]87[/C][C]0.454295[/C][C]0.908589[/C][C]0.545705[/C][/ROW]
[ROW][C]88[/C][C]0.440612[/C][C]0.881225[/C][C]0.559388[/C][/ROW]
[ROW][C]89[/C][C]0.474681[/C][C]0.949361[/C][C]0.525319[/C][/ROW]
[ROW][C]90[/C][C]0.455229[/C][C]0.910459[/C][C]0.544771[/C][/ROW]
[ROW][C]91[/C][C]0.509844[/C][C]0.980313[/C][C]0.490156[/C][/ROW]
[ROW][C]92[/C][C]0.482695[/C][C]0.965389[/C][C]0.517305[/C][/ROW]
[ROW][C]93[/C][C]0.468541[/C][C]0.937081[/C][C]0.531459[/C][/ROW]
[ROW][C]94[/C][C]0.470061[/C][C]0.940122[/C][C]0.529939[/C][/ROW]
[ROW][C]95[/C][C]0.427249[/C][C]0.854498[/C][C]0.572751[/C][/ROW]
[ROW][C]96[/C][C]0.423448[/C][C]0.846897[/C][C]0.576552[/C][/ROW]
[ROW][C]97[/C][C]0.377003[/C][C]0.754007[/C][C]0.622997[/C][/ROW]
[ROW][C]98[/C][C]0.341529[/C][C]0.683059[/C][C]0.658471[/C][/ROW]
[ROW][C]99[/C][C]0.35583[/C][C]0.711661[/C][C]0.64417[/C][/ROW]
[ROW][C]100[/C][C]0.346761[/C][C]0.693521[/C][C]0.653239[/C][/ROW]
[ROW][C]101[/C][C]0.318044[/C][C]0.636088[/C][C]0.681956[/C][/ROW]
[ROW][C]102[/C][C]0.338397[/C][C]0.676793[/C][C]0.661603[/C][/ROW]
[ROW][C]103[/C][C]0.319974[/C][C]0.639948[/C][C]0.680026[/C][/ROW]
[ROW][C]104[/C][C]0.366058[/C][C]0.732116[/C][C]0.633942[/C][/ROW]
[ROW][C]105[/C][C]0.328443[/C][C]0.656885[/C][C]0.671557[/C][/ROW]
[ROW][C]106[/C][C]0.303491[/C][C]0.606982[/C][C]0.696509[/C][/ROW]
[ROW][C]107[/C][C]0.272015[/C][C]0.544029[/C][C]0.727985[/C][/ROW]
[ROW][C]108[/C][C]0.240729[/C][C]0.481458[/C][C]0.759271[/C][/ROW]
[ROW][C]109[/C][C]0.207155[/C][C]0.414309[/C][C]0.792845[/C][/ROW]
[ROW][C]110[/C][C]0.171245[/C][C]0.342489[/C][C]0.828755[/C][/ROW]
[ROW][C]111[/C][C]0.18974[/C][C]0.37948[/C][C]0.81026[/C][/ROW]
[ROW][C]112[/C][C]0.272638[/C][C]0.545276[/C][C]0.727362[/C][/ROW]
[ROW][C]113[/C][C]0.377584[/C][C]0.755167[/C][C]0.622416[/C][/ROW]
[ROW][C]114[/C][C]0.327037[/C][C]0.654074[/C][C]0.672963[/C][/ROW]
[ROW][C]115[/C][C]0.347702[/C][C]0.695404[/C][C]0.652298[/C][/ROW]
[ROW][C]116[/C][C]0.299129[/C][C]0.598257[/C][C]0.700871[/C][/ROW]
[ROW][C]117[/C][C]0.265011[/C][C]0.530021[/C][C]0.734989[/C][/ROW]
[ROW][C]118[/C][C]0.346321[/C][C]0.692643[/C][C]0.653679[/C][/ROW]
[ROW][C]119[/C][C]0.303788[/C][C]0.607575[/C][C]0.696212[/C][/ROW]
[ROW][C]120[/C][C]0.315818[/C][C]0.631636[/C][C]0.684182[/C][/ROW]
[ROW][C]121[/C][C]0.27389[/C][C]0.547781[/C][C]0.72611[/C][/ROW]
[ROW][C]122[/C][C]0.224215[/C][C]0.448429[/C][C]0.775785[/C][/ROW]
[ROW][C]123[/C][C]0.187205[/C][C]0.37441[/C][C]0.812795[/C][/ROW]
[ROW][C]124[/C][C]0.185686[/C][C]0.371372[/C][C]0.814314[/C][/ROW]
[ROW][C]125[/C][C]0.171471[/C][C]0.342942[/C][C]0.828529[/C][/ROW]
[ROW][C]126[/C][C]0.130985[/C][C]0.26197[/C][C]0.869015[/C][/ROW]
[ROW][C]127[/C][C]0.101074[/C][C]0.202148[/C][C]0.898926[/C][/ROW]
[ROW][C]128[/C][C]0.192837[/C][C]0.385673[/C][C]0.807163[/C][/ROW]
[ROW][C]129[/C][C]0.16038[/C][C]0.320761[/C][C]0.83962[/C][/ROW]
[ROW][C]130[/C][C]0.191309[/C][C]0.382618[/C][C]0.808691[/C][/ROW]
[ROW][C]131[/C][C]0.268289[/C][C]0.536579[/C][C]0.731711[/C][/ROW]
[ROW][C]132[/C][C]0.49117[/C][C]0.982339[/C][C]0.50883[/C][/ROW]
[ROW][C]133[/C][C]0.415025[/C][C]0.83005[/C][C]0.584975[/C][/ROW]
[ROW][C]134[/C][C]0.321221[/C][C]0.642443[/C][C]0.678779[/C][/ROW]
[ROW][C]135[/C][C]0.448094[/C][C]0.896188[/C][C]0.551906[/C][/ROW]
[ROW][C]136[/C][C]0.766319[/C][C]0.467363[/C][C]0.233681[/C][/ROW]
[ROW][C]137[/C][C]0.647527[/C][C]0.704947[/C][C]0.352473[/C][/ROW]
[ROW][C]138[/C][C]0.945421[/C][C]0.109159[/C][C]0.0545793[/C][/ROW]
[ROW][C]139[/C][C]0.862192[/C][C]0.275615[/C][C]0.137808[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280313&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280313&T=5

As an alternative you can also use a QR Code:  
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The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.3632080.7264160.636792
110.2617990.5235980.738201
120.167060.334120.83294
130.3538710.7077420.646129
140.2491540.4983080.750846
150.169180.3383590.83082
160.184730.3694610.81527
170.1265190.2530380.873481
180.09664650.1932930.903354
190.06795960.1359190.93204
200.04966190.09932370.950338
210.03061050.06122090.96939
220.01886830.03773660.981132
230.01570460.03140920.984295
240.01241370.02482740.987586
250.008380190.01676040.99162
260.005109210.01021840.994891
270.00304350.006087010.996956
280.002513930.005027860.997486
290.001538890.003077790.998461
300.001198060.002396130.998802
310.0009725290.001945060.999027
320.02131530.04263060.978685
330.01447140.02894290.985529
340.01168370.02336730.988316
350.008199450.01639890.991801
360.006327860.01265570.993672
370.003999840.007999670.996
380.01231710.02463420.987683
390.009549560.01909910.99045
400.00901620.01803240.990984
410.009545470.01909090.990455
420.006975590.01395120.993024
430.005713410.01142680.994287
440.004379010.008758020.995621
450.003311130.006622250.996689
460.002935550.005871110.997064
470.002491380.004982770.997509
480.002531840.005063670.997468
490.002367470.004734930.997633
500.002035910.004071820.997964
510.002846020.005692040.997154
520.005167230.01033450.994833
530.005404040.01080810.994596
540.00415510.00831020.995845
550.003807670.007615340.996192
560.003786860.007573710.996213
570.002673230.005346470.997327
580.002004430.004008850.997996
590.00346420.006928410.996536
600.002398360.004796710.997602
610.002377250.004754490.997623
620.002167610.004335210.997832
630.003782510.007565010.996217
640.03782340.07564680.962177
650.04198850.08397710.958011
660.1106950.221390.889305
670.2153250.4306510.784675
680.297860.595720.70214
690.4639630.9279260.536037
700.4980730.9961450.501927
710.472510.9450210.52749
720.5432950.913410.456705
730.5275260.9449470.472474
740.537730.9245390.46227
750.5041640.9916710.495836
760.4989250.997850.501075
770.4621640.9243270.537836
780.4301760.8603520.569824
790.402430.8048610.59757
800.4058430.8116850.594157
810.3758240.7516480.624176
820.3621320.7242650.637868
830.3509890.7019780.649011
840.3151230.6302460.684877
850.3302480.6604950.669752
860.3778980.7557950.622102
870.4542950.9085890.545705
880.4406120.8812250.559388
890.4746810.9493610.525319
900.4552290.9104590.544771
910.5098440.9803130.490156
920.4826950.9653890.517305
930.4685410.9370810.531459
940.4700610.9401220.529939
950.4272490.8544980.572751
960.4234480.8468970.576552
970.3770030.7540070.622997
980.3415290.6830590.658471
990.355830.7116610.64417
1000.3467610.6935210.653239
1010.3180440.6360880.681956
1020.3383970.6767930.661603
1030.3199740.6399480.680026
1040.3660580.7321160.633942
1050.3284430.6568850.671557
1060.3034910.6069820.696509
1070.2720150.5440290.727985
1080.2407290.4814580.759271
1090.2071550.4143090.792845
1100.1712450.3424890.828755
1110.189740.379480.81026
1120.2726380.5452760.727362
1130.3775840.7551670.622416
1140.3270370.6540740.672963
1150.3477020.6954040.652298
1160.2991290.5982570.700871
1170.2650110.5300210.734989
1180.3463210.6926430.653679
1190.3037880.6075750.696212
1200.3158180.6316360.684182
1210.273890.5477810.72611
1220.2242150.4484290.775785
1230.1872050.374410.812795
1240.1856860.3713720.814314
1250.1714710.3429420.828529
1260.1309850.261970.869015
1270.1010740.2021480.898926
1280.1928370.3856730.807163
1290.160380.3207610.83962
1300.1913090.3826180.808691
1310.2682890.5365790.731711
1320.491170.9823390.50883
1330.4150250.830050.584975
1340.3212210.6424430.678779
1350.4480940.8961880.551906
1360.7663190.4673630.233681
1370.6475270.7049470.352473
1380.9454210.1091590.0545793
1390.8621920.2756150.137808






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level240.184615NOK
5% type I error level420.323077NOK
10% type I error level460.353846NOK

\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 & 24 & 0.184615 & NOK \tabularnewline
5% type I error level & 42 & 0.323077 & NOK \tabularnewline
10% type I error level & 46 & 0.353846 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280313&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]24[/C][C]0.184615[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]42[/C][C]0.323077[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]46[/C][C]0.353846[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280313&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280313&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 level240.184615NOK
5% type I error level420.323077NOK
10% type I error level460.353846NOK


Figures (Output of Computation)

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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')
}