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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 computationMon, 15 Dec 2014 19:11:11 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/15/t14186706772nq2fqobxptbavb.htm/, Retrieved Thu, 16 May 2024 06:19:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=268927, Retrieved Thu, 16 May 2024 06:19:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact58

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] [] [2014-12-15 19:11:11] [457d039f1491608548baeb848eb0333c] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
22	20	20	24	24	24	11,3
21	16	20	25	20	23	16,1
20	19	17	19	24	21	12,7
14	9	7	25	18	22	12,3
18	22	16	25	26	24	11,6
23	24	20	28	20	25	12,1
17	13	8	19	17	23	12,6
20	15	14	23	18	25	10,5
19	21	9	26	20	26	10,9
21	17	21	26	21	24	4,3
15	18	7	16	21	22	10,3
22	25	16	25	25	28	11,4
14	12	8	20	21	24	5,6
11	16	5	20	22	23	8,8
22	23	22	24	24	23	9,0
25	19	17	27	18	27	9,6
22	18	20	23	19	24	6,4
22	23	18	24	22	23	11,6
24	16	12	26	11	20	16,6
22	16	17	18	20	18	12,6
26	25	21	28	22	21	18,9
11	12	10	14	19	23	11,6
24	20	22	27	8	27	14,6
28	19	19	24	15	20	13,85
23	22	19	22	14	21	14,85
19	12	9	21	21	24	11,75
18	17	11	24	18	21	18,45
23	18	17	26	18	25	15,9
17	24	10	17	17	14	19,9
15	18	17	23	20	23	10,95
21	18	13	21	24	28	18,45
20	23	11	21	22	24	15,1
26	21	19	24	15	22	15
19	21	21	22	22	24	11,35
28	28	24	24	26	25	15,95
21	17	13	24	17	21	18,1
19	21	16	24	23	22	14,6
20	18	15	24	23	27	17,6
17	17	13	23	16	24	15,35
20	18	12	24	13	21	13,4
17	14	8	21	18	19	13,9
21	20	17	23	21	28	15,25
12	14	9	20	23	19	12,9
23	17	18	23	16	23	16,1
22	21	17	23	17	25	17,35
22	23	17	23	20	26	13,15
21	24	18	23	18	25	12,15
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
22	21	21	21	23	22	18,2
20	8	10	25	9	21	13,6
17	17	16	17	13	17	14,85
16	17	15	23	22	17	14,1
19	16	12	27	12	25	14,9
23	22	21	27	18	19	16,25
22	21	20	19	17	14	13,6
15	20	9	23	22	25	15,65
21	8	14	25	19	25	14,6
18	11	9	16	17	15	12,65
20	13	12	25	24	24	19,2
21	18	11	26	20	28	16,6
21	19	14	24	18	24	11,2
22	22	11	27	25	26	13,2
15	11	11	19	16	25	15,85
19	14	13	20	23	26	11,15
18	21	12	21	24	20	15,65
20	21	23	26	26	26	7,65
18	18	11	21	21	21	15,2
22	21	19	25	23	23	15,6
25	23	19	27	28	25	13,1
23	20	13	26	24	23	11,85
21	21	23	25	22	22	12,4
19	18	13	28	28	28	11,4
21	19	17	22	18	24	14,9
16	18	13	21	23	14	19,9
21	18	8	24	15	20	11,2
22	19	16	26	24	28	14,6
18	18	14	23	18	26	14,75
4	11	7	15	20	25	15,15
22	20	17	22	20	24	16,85
17	20	19	25	25	24	7,85
20	21	12	24	25	23	12,6
18	12	12	21	14	20	7,85
19	15	18	17	16	16	10,95
20	18	16	26	24	24	12,35
15	14	15	20	13	20	9,95
24	18	20	22	19	23	14,9
21	16	16	24	18	23	16,65
19	19	12	23	16	18	13,4
19	7	10	22	8	21	13,95
27	21	28	28	27	25	15,7
23	24	19	21	23	23	16,85
23	21	18	24	20	26	10,95
20	20	19	28	20	26	15,35
17	22	8	25	26	24	12,2
21	17	17	24	23	23	15,1
23	19	16	24	24	21	17,75
22	20	18	21	21	23	15,2
20	20	17	26	22	23	16,65
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 time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268927&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268927&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268927&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TOTAAL[t] = + 12.8819 + 0.228712I1[t] + 0.127218I2[t] -0.150896I3[t] + 0.0380645E1[t] -0.0101884E2[t] -0.20142E3[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOTAAL[t] =  +  12.8819 +  0.228712I1[t] +  0.127218I2[t] -0.150896I3[t] +  0.0380645E1[t] -0.0101884E2[t] -0.20142E3[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268927&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOTAAL[t] =  +  12.8819 +  0.228712I1[t] +  0.127218I2[t] -0.150896I3[t] +  0.0380645E1[t] -0.0101884E2[t] -0.20142E3[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268927&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268927&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
TOTAAL[t] = + 12.8819 + 0.228712I1[t] + 0.127218I2[t] -0.150896I3[t] + 0.0380645E1[t] -0.0101884E2[t] -0.20142E3[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)12.88192.870244.4882.01605e-051.00803e-05
I10.2287120.1347581.6970.092930.046465
I20.1272180.1047571.2140.2275990.1138
I3-0.1508960.0947264-1.5930.1144890.0572447
E10.03806450.1305970.29150.771330.385665
E2-0.01018840.0847901-0.12020.904610.452305
E3-0.201420.114121-1.7650.08078250.0403913

\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) & 12.8819 & 2.87024 & 4.488 & 2.01605e-05 & 1.00803e-05 \tabularnewline
I1 & 0.228712 & 0.134758 & 1.697 & 0.09293 & 0.046465 \tabularnewline
I2 & 0.127218 & 0.104757 & 1.214 & 0.227599 & 0.1138 \tabularnewline
I3 & -0.150896 & 0.0947264 & -1.593 & 0.114489 & 0.0572447 \tabularnewline
E1 & 0.0380645 & 0.130597 & 0.2915 & 0.77133 & 0.385665 \tabularnewline
E2 & -0.0101884 & 0.0847901 & -0.1202 & 0.90461 & 0.452305 \tabularnewline
E3 & -0.20142 & 0.114121 & -1.765 & 0.0807825 & 0.0403913 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268927&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]12.8819[/C][C]2.87024[/C][C]4.488[/C][C]2.01605e-05[/C][C]1.00803e-05[/C][/ROW]
[ROW][C]I1[/C][C]0.228712[/C][C]0.134758[/C][C]1.697[/C][C]0.09293[/C][C]0.046465[/C][/ROW]
[ROW][C]I2[/C][C]0.127218[/C][C]0.104757[/C][C]1.214[/C][C]0.227599[/C][C]0.1138[/C][/ROW]
[ROW][C]I3[/C][C]-0.150896[/C][C]0.0947264[/C][C]-1.593[/C][C]0.114489[/C][C]0.0572447[/C][/ROW]
[ROW][C]E1[/C][C]0.0380645[/C][C]0.130597[/C][C]0.2915[/C][C]0.77133[/C][C]0.385665[/C][/ROW]
[ROW][C]E2[/C][C]-0.0101884[/C][C]0.0847901[/C][C]-0.1202[/C][C]0.90461[/C][C]0.452305[/C][/ROW]
[ROW][C]E3[/C][C]-0.20142[/C][C]0.114121[/C][C]-1.765[/C][C]0.0807825[/C][C]0.0403913[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268927&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268927&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)12.88192.870244.4882.01605e-051.00803e-05
I10.2287120.1347581.6970.092930.046465
I20.1272180.1047571.2140.2275990.1138
I3-0.1508960.0947264-1.5930.1144890.0572447
E10.03806450.1305970.29150.771330.385665
E2-0.01018840.0847901-0.12020.904610.452305
E3-0.201420.114121-1.7650.08078250.0403913






Multiple Linear Regression - Regression Statistics
Multiple R0.319748
R-squared0.102239
Adjusted R-squared0.0455381
F-TEST (value)1.80313
F-TEST (DF numerator)6
F-TEST (DF denominator)95
p-value0.106638
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.04509
Sum Squared Residuals880.895

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.319748 \tabularnewline
R-squared & 0.102239 \tabularnewline
Adjusted R-squared & 0.0455381 \tabularnewline
F-TEST (value) & 1.80313 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 95 \tabularnewline
p-value & 0.106638 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.04509 \tabularnewline
Sum Squared Residuals & 880.895 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268927&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.319748[/C][/ROW]
[ROW][C]R-squared[/C][C]0.102239[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0455381[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.80313[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]95[/C][/ROW]
[ROW][C]p-value[/C][C]0.106638[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.04509[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]880.895[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268927&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268927&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 R0.319748
R-squared0.102239
Adjusted R-squared0.0455381
F-TEST (value)1.80313
F-TEST (DF numerator)6
F-TEST (DF denominator)95
p-value0.106638
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.04509
Sum Squared Residuals880.895






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
111.313.275-1.97501
216.112.81773.28234
312.713.557-0.85699
412.312.5096-0.209592
511.613.2359-1.63587
612.114.0042-1.90418
712.613.1341-0.534087
810.512.9085-2.40851
910.914.09-3.18999
104.312.6204-8.32044
1110.313.5101-3.21012
1211.413.7369-2.33688
135.612.1166-6.51662
148.812.5833-3.78328
15913.5563-4.55629
169.613.8577-4.25768
176.413.0334-6.63345
1811.614.1803-2.58025
1916.615.4451.15501
2012.614.2397-1.63971
2118.915.45193.44805
2211.611.12210.477897
2314.613.10361.49641
2413.8515.5683-1.71834
2514.8514.53910.310933
2611.7513.1474-1.39735
2718.4514.0024.44804
2815.913.63782.26219
2919.915.96833.93165
3010.9512.0764-1.12638
3118.4512.92835.52174
3215.114.46350.636516
331514.96250.0374935
3411.3512.5094-1.15944
3515.9514.83961.11035
3618.114.39653.70351
3714.613.73270.8673
3817.612.72364.87645
3915.3512.84952.50049
4013.414.4866-1.08665
4113.914.1329-0.232926
4215.2512.68582.5642
4312.912.74950.150539
4416.113.66872.43128
4517.3513.68673.66325
4613.1513.7092-0.559198
4712.1513.6786-1.5286
4812.614.1055-1.5055
4910.3512.3628-2.01284
5015.413.27112.12894
519.613.5653-3.96535
5218.213.55024.64984
5313.613.59510.00492432
5414.8513.60891.24106
5514.113.66780.432189
5614.913.32221.5778
5716.2514.78971.46032
5813.615.2974-1.69742
5915.6513.11482.53523
6014.612.31262.28736
6112.6514.4546-1.80463
6219.213.17236.02771
6316.613.46113.13887
6411.213.8856-2.68558
6513.214.5887-1.38867
6615.8511.57694.27311
6711.1512.3369-1.18692
6815.6514.3861.26397
697.6512.145-4.49502
7015.213.98441.21558
7115.613.80281.79721
7213.114.3657-1.26571
7311.8514.8375-2.98754
7412.413.1821-0.782106
7511.412.6965-1.29653
7614.913.35681.54323
7719.914.61485.28524
7811.215.5-4.29999
7914.613.02181.57818
8014.7512.63132.11868
8115.159.471625.67838
8216.8513.69233.15768
837.8512.3102-4.46022
8412.614.3432-1.7432
857.8513.343-5.49295
8610.9513.681-2.73099
8712.3513.2429-0.892859
889.9512.4307-2.48069
8914.913.65421.24577
9016.6513.40363.24644
9113.414.9208-1.52078
9213.9513.13510.81486
9315.713.25892.44111
9416.8514.26092.5891
9510.9513.5706-2.62064
9615.3512.75862.59135
9712.214.2143-2.01432
9815.113.32891.77106
9917.7514.58433.16565
10015.213.69461.50541
10116.6513.56823.0818
1028.112.1354-4.03543

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 11.3 & 13.275 & -1.97501 \tabularnewline
2 & 16.1 & 12.8177 & 3.28234 \tabularnewline
3 & 12.7 & 13.557 & -0.85699 \tabularnewline
4 & 12.3 & 12.5096 & -0.209592 \tabularnewline
5 & 11.6 & 13.2359 & -1.63587 \tabularnewline
6 & 12.1 & 14.0042 & -1.90418 \tabularnewline
7 & 12.6 & 13.1341 & -0.534087 \tabularnewline
8 & 10.5 & 12.9085 & -2.40851 \tabularnewline
9 & 10.9 & 14.09 & -3.18999 \tabularnewline
10 & 4.3 & 12.6204 & -8.32044 \tabularnewline
11 & 10.3 & 13.5101 & -3.21012 \tabularnewline
12 & 11.4 & 13.7369 & -2.33688 \tabularnewline
13 & 5.6 & 12.1166 & -6.51662 \tabularnewline
14 & 8.8 & 12.5833 & -3.78328 \tabularnewline
15 & 9 & 13.5563 & -4.55629 \tabularnewline
16 & 9.6 & 13.8577 & -4.25768 \tabularnewline
17 & 6.4 & 13.0334 & -6.63345 \tabularnewline
18 & 11.6 & 14.1803 & -2.58025 \tabularnewline
19 & 16.6 & 15.445 & 1.15501 \tabularnewline
20 & 12.6 & 14.2397 & -1.63971 \tabularnewline
21 & 18.9 & 15.4519 & 3.44805 \tabularnewline
22 & 11.6 & 11.1221 & 0.477897 \tabularnewline
23 & 14.6 & 13.1036 & 1.49641 \tabularnewline
24 & 13.85 & 15.5683 & -1.71834 \tabularnewline
25 & 14.85 & 14.5391 & 0.310933 \tabularnewline
26 & 11.75 & 13.1474 & -1.39735 \tabularnewline
27 & 18.45 & 14.002 & 4.44804 \tabularnewline
28 & 15.9 & 13.6378 & 2.26219 \tabularnewline
29 & 19.9 & 15.9683 & 3.93165 \tabularnewline
30 & 10.95 & 12.0764 & -1.12638 \tabularnewline
31 & 18.45 & 12.9283 & 5.52174 \tabularnewline
32 & 15.1 & 14.4635 & 0.636516 \tabularnewline
33 & 15 & 14.9625 & 0.0374935 \tabularnewline
34 & 11.35 & 12.5094 & -1.15944 \tabularnewline
35 & 15.95 & 14.8396 & 1.11035 \tabularnewline
36 & 18.1 & 14.3965 & 3.70351 \tabularnewline
37 & 14.6 & 13.7327 & 0.8673 \tabularnewline
38 & 17.6 & 12.7236 & 4.87645 \tabularnewline
39 & 15.35 & 12.8495 & 2.50049 \tabularnewline
40 & 13.4 & 14.4866 & -1.08665 \tabularnewline
41 & 13.9 & 14.1329 & -0.232926 \tabularnewline
42 & 15.25 & 12.6858 & 2.5642 \tabularnewline
43 & 12.9 & 12.7495 & 0.150539 \tabularnewline
44 & 16.1 & 13.6687 & 2.43128 \tabularnewline
45 & 17.35 & 13.6867 & 3.66325 \tabularnewline
46 & 13.15 & 13.7092 & -0.559198 \tabularnewline
47 & 12.15 & 13.6786 & -1.5286 \tabularnewline
48 & 12.6 & 14.1055 & -1.5055 \tabularnewline
49 & 10.35 & 12.3628 & -2.01284 \tabularnewline
50 & 15.4 & 13.2711 & 2.12894 \tabularnewline
51 & 9.6 & 13.5653 & -3.96535 \tabularnewline
52 & 18.2 & 13.5502 & 4.64984 \tabularnewline
53 & 13.6 & 13.5951 & 0.00492432 \tabularnewline
54 & 14.85 & 13.6089 & 1.24106 \tabularnewline
55 & 14.1 & 13.6678 & 0.432189 \tabularnewline
56 & 14.9 & 13.3222 & 1.5778 \tabularnewline
57 & 16.25 & 14.7897 & 1.46032 \tabularnewline
58 & 13.6 & 15.2974 & -1.69742 \tabularnewline
59 & 15.65 & 13.1148 & 2.53523 \tabularnewline
60 & 14.6 & 12.3126 & 2.28736 \tabularnewline
61 & 12.65 & 14.4546 & -1.80463 \tabularnewline
62 & 19.2 & 13.1723 & 6.02771 \tabularnewline
63 & 16.6 & 13.4611 & 3.13887 \tabularnewline
64 & 11.2 & 13.8856 & -2.68558 \tabularnewline
65 & 13.2 & 14.5887 & -1.38867 \tabularnewline
66 & 15.85 & 11.5769 & 4.27311 \tabularnewline
67 & 11.15 & 12.3369 & -1.18692 \tabularnewline
68 & 15.65 & 14.386 & 1.26397 \tabularnewline
69 & 7.65 & 12.145 & -4.49502 \tabularnewline
70 & 15.2 & 13.9844 & 1.21558 \tabularnewline
71 & 15.6 & 13.8028 & 1.79721 \tabularnewline
72 & 13.1 & 14.3657 & -1.26571 \tabularnewline
73 & 11.85 & 14.8375 & -2.98754 \tabularnewline
74 & 12.4 & 13.1821 & -0.782106 \tabularnewline
75 & 11.4 & 12.6965 & -1.29653 \tabularnewline
76 & 14.9 & 13.3568 & 1.54323 \tabularnewline
77 & 19.9 & 14.6148 & 5.28524 \tabularnewline
78 & 11.2 & 15.5 & -4.29999 \tabularnewline
79 & 14.6 & 13.0218 & 1.57818 \tabularnewline
80 & 14.75 & 12.6313 & 2.11868 \tabularnewline
81 & 15.15 & 9.47162 & 5.67838 \tabularnewline
82 & 16.85 & 13.6923 & 3.15768 \tabularnewline
83 & 7.85 & 12.3102 & -4.46022 \tabularnewline
84 & 12.6 & 14.3432 & -1.7432 \tabularnewline
85 & 7.85 & 13.343 & -5.49295 \tabularnewline
86 & 10.95 & 13.681 & -2.73099 \tabularnewline
87 & 12.35 & 13.2429 & -0.892859 \tabularnewline
88 & 9.95 & 12.4307 & -2.48069 \tabularnewline
89 & 14.9 & 13.6542 & 1.24577 \tabularnewline
90 & 16.65 & 13.4036 & 3.24644 \tabularnewline
91 & 13.4 & 14.9208 & -1.52078 \tabularnewline
92 & 13.95 & 13.1351 & 0.81486 \tabularnewline
93 & 15.7 & 13.2589 & 2.44111 \tabularnewline
94 & 16.85 & 14.2609 & 2.5891 \tabularnewline
95 & 10.95 & 13.5706 & -2.62064 \tabularnewline
96 & 15.35 & 12.7586 & 2.59135 \tabularnewline
97 & 12.2 & 14.2143 & -2.01432 \tabularnewline
98 & 15.1 & 13.3289 & 1.77106 \tabularnewline
99 & 17.75 & 14.5843 & 3.16565 \tabularnewline
100 & 15.2 & 13.6946 & 1.50541 \tabularnewline
101 & 16.65 & 13.5682 & 3.0818 \tabularnewline
102 & 8.1 & 12.1354 & -4.03543 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268927&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]11.3[/C][C]13.275[/C][C]-1.97501[/C][/ROW]
[ROW][C]2[/C][C]16.1[/C][C]12.8177[/C][C]3.28234[/C][/ROW]
[ROW][C]3[/C][C]12.7[/C][C]13.557[/C][C]-0.85699[/C][/ROW]
[ROW][C]4[/C][C]12.3[/C][C]12.5096[/C][C]-0.209592[/C][/ROW]
[ROW][C]5[/C][C]11.6[/C][C]13.2359[/C][C]-1.63587[/C][/ROW]
[ROW][C]6[/C][C]12.1[/C][C]14.0042[/C][C]-1.90418[/C][/ROW]
[ROW][C]7[/C][C]12.6[/C][C]13.1341[/C][C]-0.534087[/C][/ROW]
[ROW][C]8[/C][C]10.5[/C][C]12.9085[/C][C]-2.40851[/C][/ROW]
[ROW][C]9[/C][C]10.9[/C][C]14.09[/C][C]-3.18999[/C][/ROW]
[ROW][C]10[/C][C]4.3[/C][C]12.6204[/C][C]-8.32044[/C][/ROW]
[ROW][C]11[/C][C]10.3[/C][C]13.5101[/C][C]-3.21012[/C][/ROW]
[ROW][C]12[/C][C]11.4[/C][C]13.7369[/C][C]-2.33688[/C][/ROW]
[ROW][C]13[/C][C]5.6[/C][C]12.1166[/C][C]-6.51662[/C][/ROW]
[ROW][C]14[/C][C]8.8[/C][C]12.5833[/C][C]-3.78328[/C][/ROW]
[ROW][C]15[/C][C]9[/C][C]13.5563[/C][C]-4.55629[/C][/ROW]
[ROW][C]16[/C][C]9.6[/C][C]13.8577[/C][C]-4.25768[/C][/ROW]
[ROW][C]17[/C][C]6.4[/C][C]13.0334[/C][C]-6.63345[/C][/ROW]
[ROW][C]18[/C][C]11.6[/C][C]14.1803[/C][C]-2.58025[/C][/ROW]
[ROW][C]19[/C][C]16.6[/C][C]15.445[/C][C]1.15501[/C][/ROW]
[ROW][C]20[/C][C]12.6[/C][C]14.2397[/C][C]-1.63971[/C][/ROW]
[ROW][C]21[/C][C]18.9[/C][C]15.4519[/C][C]3.44805[/C][/ROW]
[ROW][C]22[/C][C]11.6[/C][C]11.1221[/C][C]0.477897[/C][/ROW]
[ROW][C]23[/C][C]14.6[/C][C]13.1036[/C][C]1.49641[/C][/ROW]
[ROW][C]24[/C][C]13.85[/C][C]15.5683[/C][C]-1.71834[/C][/ROW]
[ROW][C]25[/C][C]14.85[/C][C]14.5391[/C][C]0.310933[/C][/ROW]
[ROW][C]26[/C][C]11.75[/C][C]13.1474[/C][C]-1.39735[/C][/ROW]
[ROW][C]27[/C][C]18.45[/C][C]14.002[/C][C]4.44804[/C][/ROW]
[ROW][C]28[/C][C]15.9[/C][C]13.6378[/C][C]2.26219[/C][/ROW]
[ROW][C]29[/C][C]19.9[/C][C]15.9683[/C][C]3.93165[/C][/ROW]
[ROW][C]30[/C][C]10.95[/C][C]12.0764[/C][C]-1.12638[/C][/ROW]
[ROW][C]31[/C][C]18.45[/C][C]12.9283[/C][C]5.52174[/C][/ROW]
[ROW][C]32[/C][C]15.1[/C][C]14.4635[/C][C]0.636516[/C][/ROW]
[ROW][C]33[/C][C]15[/C][C]14.9625[/C][C]0.0374935[/C][/ROW]
[ROW][C]34[/C][C]11.35[/C][C]12.5094[/C][C]-1.15944[/C][/ROW]
[ROW][C]35[/C][C]15.95[/C][C]14.8396[/C][C]1.11035[/C][/ROW]
[ROW][C]36[/C][C]18.1[/C][C]14.3965[/C][C]3.70351[/C][/ROW]
[ROW][C]37[/C][C]14.6[/C][C]13.7327[/C][C]0.8673[/C][/ROW]
[ROW][C]38[/C][C]17.6[/C][C]12.7236[/C][C]4.87645[/C][/ROW]
[ROW][C]39[/C][C]15.35[/C][C]12.8495[/C][C]2.50049[/C][/ROW]
[ROW][C]40[/C][C]13.4[/C][C]14.4866[/C][C]-1.08665[/C][/ROW]
[ROW][C]41[/C][C]13.9[/C][C]14.1329[/C][C]-0.232926[/C][/ROW]
[ROW][C]42[/C][C]15.25[/C][C]12.6858[/C][C]2.5642[/C][/ROW]
[ROW][C]43[/C][C]12.9[/C][C]12.7495[/C][C]0.150539[/C][/ROW]
[ROW][C]44[/C][C]16.1[/C][C]13.6687[/C][C]2.43128[/C][/ROW]
[ROW][C]45[/C][C]17.35[/C][C]13.6867[/C][C]3.66325[/C][/ROW]
[ROW][C]46[/C][C]13.15[/C][C]13.7092[/C][C]-0.559198[/C][/ROW]
[ROW][C]47[/C][C]12.15[/C][C]13.6786[/C][C]-1.5286[/C][/ROW]
[ROW][C]48[/C][C]12.6[/C][C]14.1055[/C][C]-1.5055[/C][/ROW]
[ROW][C]49[/C][C]10.35[/C][C]12.3628[/C][C]-2.01284[/C][/ROW]
[ROW][C]50[/C][C]15.4[/C][C]13.2711[/C][C]2.12894[/C][/ROW]
[ROW][C]51[/C][C]9.6[/C][C]13.5653[/C][C]-3.96535[/C][/ROW]
[ROW][C]52[/C][C]18.2[/C][C]13.5502[/C][C]4.64984[/C][/ROW]
[ROW][C]53[/C][C]13.6[/C][C]13.5951[/C][C]0.00492432[/C][/ROW]
[ROW][C]54[/C][C]14.85[/C][C]13.6089[/C][C]1.24106[/C][/ROW]
[ROW][C]55[/C][C]14.1[/C][C]13.6678[/C][C]0.432189[/C][/ROW]
[ROW][C]56[/C][C]14.9[/C][C]13.3222[/C][C]1.5778[/C][/ROW]
[ROW][C]57[/C][C]16.25[/C][C]14.7897[/C][C]1.46032[/C][/ROW]
[ROW][C]58[/C][C]13.6[/C][C]15.2974[/C][C]-1.69742[/C][/ROW]
[ROW][C]59[/C][C]15.65[/C][C]13.1148[/C][C]2.53523[/C][/ROW]
[ROW][C]60[/C][C]14.6[/C][C]12.3126[/C][C]2.28736[/C][/ROW]
[ROW][C]61[/C][C]12.65[/C][C]14.4546[/C][C]-1.80463[/C][/ROW]
[ROW][C]62[/C][C]19.2[/C][C]13.1723[/C][C]6.02771[/C][/ROW]
[ROW][C]63[/C][C]16.6[/C][C]13.4611[/C][C]3.13887[/C][/ROW]
[ROW][C]64[/C][C]11.2[/C][C]13.8856[/C][C]-2.68558[/C][/ROW]
[ROW][C]65[/C][C]13.2[/C][C]14.5887[/C][C]-1.38867[/C][/ROW]
[ROW][C]66[/C][C]15.85[/C][C]11.5769[/C][C]4.27311[/C][/ROW]
[ROW][C]67[/C][C]11.15[/C][C]12.3369[/C][C]-1.18692[/C][/ROW]
[ROW][C]68[/C][C]15.65[/C][C]14.386[/C][C]1.26397[/C][/ROW]
[ROW][C]69[/C][C]7.65[/C][C]12.145[/C][C]-4.49502[/C][/ROW]
[ROW][C]70[/C][C]15.2[/C][C]13.9844[/C][C]1.21558[/C][/ROW]
[ROW][C]71[/C][C]15.6[/C][C]13.8028[/C][C]1.79721[/C][/ROW]
[ROW][C]72[/C][C]13.1[/C][C]14.3657[/C][C]-1.26571[/C][/ROW]
[ROW][C]73[/C][C]11.85[/C][C]14.8375[/C][C]-2.98754[/C][/ROW]
[ROW][C]74[/C][C]12.4[/C][C]13.1821[/C][C]-0.782106[/C][/ROW]
[ROW][C]75[/C][C]11.4[/C][C]12.6965[/C][C]-1.29653[/C][/ROW]
[ROW][C]76[/C][C]14.9[/C][C]13.3568[/C][C]1.54323[/C][/ROW]
[ROW][C]77[/C][C]19.9[/C][C]14.6148[/C][C]5.28524[/C][/ROW]
[ROW][C]78[/C][C]11.2[/C][C]15.5[/C][C]-4.29999[/C][/ROW]
[ROW][C]79[/C][C]14.6[/C][C]13.0218[/C][C]1.57818[/C][/ROW]
[ROW][C]80[/C][C]14.75[/C][C]12.6313[/C][C]2.11868[/C][/ROW]
[ROW][C]81[/C][C]15.15[/C][C]9.47162[/C][C]5.67838[/C][/ROW]
[ROW][C]82[/C][C]16.85[/C][C]13.6923[/C][C]3.15768[/C][/ROW]
[ROW][C]83[/C][C]7.85[/C][C]12.3102[/C][C]-4.46022[/C][/ROW]
[ROW][C]84[/C][C]12.6[/C][C]14.3432[/C][C]-1.7432[/C][/ROW]
[ROW][C]85[/C][C]7.85[/C][C]13.343[/C][C]-5.49295[/C][/ROW]
[ROW][C]86[/C][C]10.95[/C][C]13.681[/C][C]-2.73099[/C][/ROW]
[ROW][C]87[/C][C]12.35[/C][C]13.2429[/C][C]-0.892859[/C][/ROW]
[ROW][C]88[/C][C]9.95[/C][C]12.4307[/C][C]-2.48069[/C][/ROW]
[ROW][C]89[/C][C]14.9[/C][C]13.6542[/C][C]1.24577[/C][/ROW]
[ROW][C]90[/C][C]16.65[/C][C]13.4036[/C][C]3.24644[/C][/ROW]
[ROW][C]91[/C][C]13.4[/C][C]14.9208[/C][C]-1.52078[/C][/ROW]
[ROW][C]92[/C][C]13.95[/C][C]13.1351[/C][C]0.81486[/C][/ROW]
[ROW][C]93[/C][C]15.7[/C][C]13.2589[/C][C]2.44111[/C][/ROW]
[ROW][C]94[/C][C]16.85[/C][C]14.2609[/C][C]2.5891[/C][/ROW]
[ROW][C]95[/C][C]10.95[/C][C]13.5706[/C][C]-2.62064[/C][/ROW]
[ROW][C]96[/C][C]15.35[/C][C]12.7586[/C][C]2.59135[/C][/ROW]
[ROW][C]97[/C][C]12.2[/C][C]14.2143[/C][C]-2.01432[/C][/ROW]
[ROW][C]98[/C][C]15.1[/C][C]13.3289[/C][C]1.77106[/C][/ROW]
[ROW][C]99[/C][C]17.75[/C][C]14.5843[/C][C]3.16565[/C][/ROW]
[ROW][C]100[/C][C]15.2[/C][C]13.6946[/C][C]1.50541[/C][/ROW]
[ROW][C]101[/C][C]16.65[/C][C]13.5682[/C][C]3.0818[/C][/ROW]
[ROW][C]102[/C][C]8.1[/C][C]12.1354[/C][C]-4.03543[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268927&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268927&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
111.313.275-1.97501
216.112.81773.28234
312.713.557-0.85699
412.312.5096-0.209592
511.613.2359-1.63587
612.114.0042-1.90418
712.613.1341-0.534087
810.512.9085-2.40851
910.914.09-3.18999
104.312.6204-8.32044
1110.313.5101-3.21012
1211.413.7369-2.33688
135.612.1166-6.51662
148.812.5833-3.78328
15913.5563-4.55629
169.613.8577-4.25768
176.413.0334-6.63345
1811.614.1803-2.58025
1916.615.4451.15501
2012.614.2397-1.63971
2118.915.45193.44805
2211.611.12210.477897
2314.613.10361.49641
2413.8515.5683-1.71834
2514.8514.53910.310933
2611.7513.1474-1.39735
2718.4514.0024.44804
2815.913.63782.26219
2919.915.96833.93165
3010.9512.0764-1.12638
3118.4512.92835.52174
3215.114.46350.636516
331514.96250.0374935
3411.3512.5094-1.15944
3515.9514.83961.11035
3618.114.39653.70351
3714.613.73270.8673
3817.612.72364.87645
3915.3512.84952.50049
4013.414.4866-1.08665
4113.914.1329-0.232926
4215.2512.68582.5642
4312.912.74950.150539
4416.113.66872.43128
4517.3513.68673.66325
4613.1513.7092-0.559198
4712.1513.6786-1.5286
4812.614.1055-1.5055
4910.3512.3628-2.01284
5015.413.27112.12894
519.613.5653-3.96535
5218.213.55024.64984
5313.613.59510.00492432
5414.8513.60891.24106
5514.113.66780.432189
5614.913.32221.5778
5716.2514.78971.46032
5813.615.2974-1.69742
5915.6513.11482.53523
6014.612.31262.28736
6112.6514.4546-1.80463
6219.213.17236.02771
6316.613.46113.13887
6411.213.8856-2.68558
6513.214.5887-1.38867
6615.8511.57694.27311
6711.1512.3369-1.18692
6815.6514.3861.26397
697.6512.145-4.49502
7015.213.98441.21558
7115.613.80281.79721
7213.114.3657-1.26571
7311.8514.8375-2.98754
7412.413.1821-0.782106
7511.412.6965-1.29653
7614.913.35681.54323
7719.914.61485.28524
7811.215.5-4.29999
7914.613.02181.57818
8014.7512.63132.11868
8115.159.471625.67838
8216.8513.69233.15768
837.8512.3102-4.46022
8412.614.3432-1.7432
857.8513.343-5.49295
8610.9513.681-2.73099
8712.3513.2429-0.892859
889.9512.4307-2.48069
8914.913.65421.24577
9016.6513.40363.24644
9113.414.9208-1.52078
9213.9513.13510.81486
9315.713.25892.44111
9416.8514.26092.5891
9510.9513.5706-2.62064
9615.3512.75862.59135
9712.214.2143-2.01432
9815.113.32891.77106
9917.7514.58433.16565
10015.213.69461.50541
10116.6513.56823.0818
1028.112.1354-4.03543






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.8911170.2177650.108883
110.8190430.3619130.180957
120.7666690.4666610.233331
130.7221560.5556880.277844
140.6786810.6426380.321319
150.6595910.6808180.340409
160.660290.679420.33971
170.6828210.6343580.317179
180.6342710.7314580.365729
190.5840330.8319350.415967
200.5465650.9068690.453435
210.4975760.9951520.502424
220.7287230.5425540.271277
230.6894950.6210090.310505
240.6457140.7085730.354286
250.5801390.8397230.419861
260.569940.860120.43006
270.650880.698240.34912
280.698860.6022810.30114
290.6803270.6393450.319673
300.6236020.7527960.376398
310.9206450.1587110.0793553
320.8953670.2092660.104633
330.8630780.2738430.136922
340.835940.3281210.16406
350.8089080.3821830.191092
360.8318190.3363620.168181
370.7999150.4001710.200085
380.89360.2127990.1064
390.8871210.2257580.112879
400.8626730.2746540.137327
410.8258750.348250.174125
420.8217930.3564140.178207
430.7868580.4262850.213142
440.7734870.4530270.226513
450.7948560.4102880.205144
460.7502920.4994170.249708
470.7112060.5775890.288794
480.6679170.6641660.332083
490.6388760.7222490.361124
500.6236120.7527770.376388
510.6545160.6909680.345484
520.7261050.5477910.273895
530.6775930.6448140.322407
540.630810.7383790.36919
550.5733130.8533740.426687
560.530220.9395590.46978
570.4880060.9760120.511994
580.4493670.8987340.550633
590.4282240.8564480.571776
600.4037930.8075870.596207
610.3702250.7404490.629775
620.5045770.9908460.495423
630.5100410.9799190.489959
640.4890810.9781620.510919
650.4395290.8790580.560471
660.4908490.9816980.509151
670.4350210.8700410.564979
680.3807840.7615680.619216
690.4947570.9895150.505243
700.444830.889660.55517
710.3951010.7902020.604899
720.3422310.6844620.657769
730.3213150.642630.678685
740.2855380.5710770.714462
750.2469390.4938770.753061
760.2051870.4103750.794813
770.4107740.8215480.589226
780.4066690.8133390.593331
790.3421060.6842130.657894
800.2804060.5608110.719594
810.8290220.3419550.170978
820.8162470.3675060.183753
830.8288030.3423940.171197
840.7834750.4330490.216525
850.8917990.2164010.108201
860.8687670.2624650.131233
870.8355970.3288060.164403
880.7830640.4338720.216936
890.680430.6391410.31957
900.6263940.7472110.373606
910.9924710.01505860.00752929
920.9705090.05898110.0294906

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.891117 & 0.217765 & 0.108883 \tabularnewline
11 & 0.819043 & 0.361913 & 0.180957 \tabularnewline
12 & 0.766669 & 0.466661 & 0.233331 \tabularnewline
13 & 0.722156 & 0.555688 & 0.277844 \tabularnewline
14 & 0.678681 & 0.642638 & 0.321319 \tabularnewline
15 & 0.659591 & 0.680818 & 0.340409 \tabularnewline
16 & 0.66029 & 0.67942 & 0.33971 \tabularnewline
17 & 0.682821 & 0.634358 & 0.317179 \tabularnewline
18 & 0.634271 & 0.731458 & 0.365729 \tabularnewline
19 & 0.584033 & 0.831935 & 0.415967 \tabularnewline
20 & 0.546565 & 0.906869 & 0.453435 \tabularnewline
21 & 0.497576 & 0.995152 & 0.502424 \tabularnewline
22 & 0.728723 & 0.542554 & 0.271277 \tabularnewline
23 & 0.689495 & 0.621009 & 0.310505 \tabularnewline
24 & 0.645714 & 0.708573 & 0.354286 \tabularnewline
25 & 0.580139 & 0.839723 & 0.419861 \tabularnewline
26 & 0.56994 & 0.86012 & 0.43006 \tabularnewline
27 & 0.65088 & 0.69824 & 0.34912 \tabularnewline
28 & 0.69886 & 0.602281 & 0.30114 \tabularnewline
29 & 0.680327 & 0.639345 & 0.319673 \tabularnewline
30 & 0.623602 & 0.752796 & 0.376398 \tabularnewline
31 & 0.920645 & 0.158711 & 0.0793553 \tabularnewline
32 & 0.895367 & 0.209266 & 0.104633 \tabularnewline
33 & 0.863078 & 0.273843 & 0.136922 \tabularnewline
34 & 0.83594 & 0.328121 & 0.16406 \tabularnewline
35 & 0.808908 & 0.382183 & 0.191092 \tabularnewline
36 & 0.831819 & 0.336362 & 0.168181 \tabularnewline
37 & 0.799915 & 0.400171 & 0.200085 \tabularnewline
38 & 0.8936 & 0.212799 & 0.1064 \tabularnewline
39 & 0.887121 & 0.225758 & 0.112879 \tabularnewline
40 & 0.862673 & 0.274654 & 0.137327 \tabularnewline
41 & 0.825875 & 0.34825 & 0.174125 \tabularnewline
42 & 0.821793 & 0.356414 & 0.178207 \tabularnewline
43 & 0.786858 & 0.426285 & 0.213142 \tabularnewline
44 & 0.773487 & 0.453027 & 0.226513 \tabularnewline
45 & 0.794856 & 0.410288 & 0.205144 \tabularnewline
46 & 0.750292 & 0.499417 & 0.249708 \tabularnewline
47 & 0.711206 & 0.577589 & 0.288794 \tabularnewline
48 & 0.667917 & 0.664166 & 0.332083 \tabularnewline
49 & 0.638876 & 0.722249 & 0.361124 \tabularnewline
50 & 0.623612 & 0.752777 & 0.376388 \tabularnewline
51 & 0.654516 & 0.690968 & 0.345484 \tabularnewline
52 & 0.726105 & 0.547791 & 0.273895 \tabularnewline
53 & 0.677593 & 0.644814 & 0.322407 \tabularnewline
54 & 0.63081 & 0.738379 & 0.36919 \tabularnewline
55 & 0.573313 & 0.853374 & 0.426687 \tabularnewline
56 & 0.53022 & 0.939559 & 0.46978 \tabularnewline
57 & 0.488006 & 0.976012 & 0.511994 \tabularnewline
58 & 0.449367 & 0.898734 & 0.550633 \tabularnewline
59 & 0.428224 & 0.856448 & 0.571776 \tabularnewline
60 & 0.403793 & 0.807587 & 0.596207 \tabularnewline
61 & 0.370225 & 0.740449 & 0.629775 \tabularnewline
62 & 0.504577 & 0.990846 & 0.495423 \tabularnewline
63 & 0.510041 & 0.979919 & 0.489959 \tabularnewline
64 & 0.489081 & 0.978162 & 0.510919 \tabularnewline
65 & 0.439529 & 0.879058 & 0.560471 \tabularnewline
66 & 0.490849 & 0.981698 & 0.509151 \tabularnewline
67 & 0.435021 & 0.870041 & 0.564979 \tabularnewline
68 & 0.380784 & 0.761568 & 0.619216 \tabularnewline
69 & 0.494757 & 0.989515 & 0.505243 \tabularnewline
70 & 0.44483 & 0.88966 & 0.55517 \tabularnewline
71 & 0.395101 & 0.790202 & 0.604899 \tabularnewline
72 & 0.342231 & 0.684462 & 0.657769 \tabularnewline
73 & 0.321315 & 0.64263 & 0.678685 \tabularnewline
74 & 0.285538 & 0.571077 & 0.714462 \tabularnewline
75 & 0.246939 & 0.493877 & 0.753061 \tabularnewline
76 & 0.205187 & 0.410375 & 0.794813 \tabularnewline
77 & 0.410774 & 0.821548 & 0.589226 \tabularnewline
78 & 0.406669 & 0.813339 & 0.593331 \tabularnewline
79 & 0.342106 & 0.684213 & 0.657894 \tabularnewline
80 & 0.280406 & 0.560811 & 0.719594 \tabularnewline
81 & 0.829022 & 0.341955 & 0.170978 \tabularnewline
82 & 0.816247 & 0.367506 & 0.183753 \tabularnewline
83 & 0.828803 & 0.342394 & 0.171197 \tabularnewline
84 & 0.783475 & 0.433049 & 0.216525 \tabularnewline
85 & 0.891799 & 0.216401 & 0.108201 \tabularnewline
86 & 0.868767 & 0.262465 & 0.131233 \tabularnewline
87 & 0.835597 & 0.328806 & 0.164403 \tabularnewline
88 & 0.783064 & 0.433872 & 0.216936 \tabularnewline
89 & 0.68043 & 0.639141 & 0.31957 \tabularnewline
90 & 0.626394 & 0.747211 & 0.373606 \tabularnewline
91 & 0.992471 & 0.0150586 & 0.00752929 \tabularnewline
92 & 0.970509 & 0.0589811 & 0.0294906 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268927&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]0.891117[/C][C]0.217765[/C][C]0.108883[/C][/ROW]
[ROW][C]11[/C][C]0.819043[/C][C]0.361913[/C][C]0.180957[/C][/ROW]
[ROW][C]12[/C][C]0.766669[/C][C]0.466661[/C][C]0.233331[/C][/ROW]
[ROW][C]13[/C][C]0.722156[/C][C]0.555688[/C][C]0.277844[/C][/ROW]
[ROW][C]14[/C][C]0.678681[/C][C]0.642638[/C][C]0.321319[/C][/ROW]
[ROW][C]15[/C][C]0.659591[/C][C]0.680818[/C][C]0.340409[/C][/ROW]
[ROW][C]16[/C][C]0.66029[/C][C]0.67942[/C][C]0.33971[/C][/ROW]
[ROW][C]17[/C][C]0.682821[/C][C]0.634358[/C][C]0.317179[/C][/ROW]
[ROW][C]18[/C][C]0.634271[/C][C]0.731458[/C][C]0.365729[/C][/ROW]
[ROW][C]19[/C][C]0.584033[/C][C]0.831935[/C][C]0.415967[/C][/ROW]
[ROW][C]20[/C][C]0.546565[/C][C]0.906869[/C][C]0.453435[/C][/ROW]
[ROW][C]21[/C][C]0.497576[/C][C]0.995152[/C][C]0.502424[/C][/ROW]
[ROW][C]22[/C][C]0.728723[/C][C]0.542554[/C][C]0.271277[/C][/ROW]
[ROW][C]23[/C][C]0.689495[/C][C]0.621009[/C][C]0.310505[/C][/ROW]
[ROW][C]24[/C][C]0.645714[/C][C]0.708573[/C][C]0.354286[/C][/ROW]
[ROW][C]25[/C][C]0.580139[/C][C]0.839723[/C][C]0.419861[/C][/ROW]
[ROW][C]26[/C][C]0.56994[/C][C]0.86012[/C][C]0.43006[/C][/ROW]
[ROW][C]27[/C][C]0.65088[/C][C]0.69824[/C][C]0.34912[/C][/ROW]
[ROW][C]28[/C][C]0.69886[/C][C]0.602281[/C][C]0.30114[/C][/ROW]
[ROW][C]29[/C][C]0.680327[/C][C]0.639345[/C][C]0.319673[/C][/ROW]
[ROW][C]30[/C][C]0.623602[/C][C]0.752796[/C][C]0.376398[/C][/ROW]
[ROW][C]31[/C][C]0.920645[/C][C]0.158711[/C][C]0.0793553[/C][/ROW]
[ROW][C]32[/C][C]0.895367[/C][C]0.209266[/C][C]0.104633[/C][/ROW]
[ROW][C]33[/C][C]0.863078[/C][C]0.273843[/C][C]0.136922[/C][/ROW]
[ROW][C]34[/C][C]0.83594[/C][C]0.328121[/C][C]0.16406[/C][/ROW]
[ROW][C]35[/C][C]0.808908[/C][C]0.382183[/C][C]0.191092[/C][/ROW]
[ROW][C]36[/C][C]0.831819[/C][C]0.336362[/C][C]0.168181[/C][/ROW]
[ROW][C]37[/C][C]0.799915[/C][C]0.400171[/C][C]0.200085[/C][/ROW]
[ROW][C]38[/C][C]0.8936[/C][C]0.212799[/C][C]0.1064[/C][/ROW]
[ROW][C]39[/C][C]0.887121[/C][C]0.225758[/C][C]0.112879[/C][/ROW]
[ROW][C]40[/C][C]0.862673[/C][C]0.274654[/C][C]0.137327[/C][/ROW]
[ROW][C]41[/C][C]0.825875[/C][C]0.34825[/C][C]0.174125[/C][/ROW]
[ROW][C]42[/C][C]0.821793[/C][C]0.356414[/C][C]0.178207[/C][/ROW]
[ROW][C]43[/C][C]0.786858[/C][C]0.426285[/C][C]0.213142[/C][/ROW]
[ROW][C]44[/C][C]0.773487[/C][C]0.453027[/C][C]0.226513[/C][/ROW]
[ROW][C]45[/C][C]0.794856[/C][C]0.410288[/C][C]0.205144[/C][/ROW]
[ROW][C]46[/C][C]0.750292[/C][C]0.499417[/C][C]0.249708[/C][/ROW]
[ROW][C]47[/C][C]0.711206[/C][C]0.577589[/C][C]0.288794[/C][/ROW]
[ROW][C]48[/C][C]0.667917[/C][C]0.664166[/C][C]0.332083[/C][/ROW]
[ROW][C]49[/C][C]0.638876[/C][C]0.722249[/C][C]0.361124[/C][/ROW]
[ROW][C]50[/C][C]0.623612[/C][C]0.752777[/C][C]0.376388[/C][/ROW]
[ROW][C]51[/C][C]0.654516[/C][C]0.690968[/C][C]0.345484[/C][/ROW]
[ROW][C]52[/C][C]0.726105[/C][C]0.547791[/C][C]0.273895[/C][/ROW]
[ROW][C]53[/C][C]0.677593[/C][C]0.644814[/C][C]0.322407[/C][/ROW]
[ROW][C]54[/C][C]0.63081[/C][C]0.738379[/C][C]0.36919[/C][/ROW]
[ROW][C]55[/C][C]0.573313[/C][C]0.853374[/C][C]0.426687[/C][/ROW]
[ROW][C]56[/C][C]0.53022[/C][C]0.939559[/C][C]0.46978[/C][/ROW]
[ROW][C]57[/C][C]0.488006[/C][C]0.976012[/C][C]0.511994[/C][/ROW]
[ROW][C]58[/C][C]0.449367[/C][C]0.898734[/C][C]0.550633[/C][/ROW]
[ROW][C]59[/C][C]0.428224[/C][C]0.856448[/C][C]0.571776[/C][/ROW]
[ROW][C]60[/C][C]0.403793[/C][C]0.807587[/C][C]0.596207[/C][/ROW]
[ROW][C]61[/C][C]0.370225[/C][C]0.740449[/C][C]0.629775[/C][/ROW]
[ROW][C]62[/C][C]0.504577[/C][C]0.990846[/C][C]0.495423[/C][/ROW]
[ROW][C]63[/C][C]0.510041[/C][C]0.979919[/C][C]0.489959[/C][/ROW]
[ROW][C]64[/C][C]0.489081[/C][C]0.978162[/C][C]0.510919[/C][/ROW]
[ROW][C]65[/C][C]0.439529[/C][C]0.879058[/C][C]0.560471[/C][/ROW]
[ROW][C]66[/C][C]0.490849[/C][C]0.981698[/C][C]0.509151[/C][/ROW]
[ROW][C]67[/C][C]0.435021[/C][C]0.870041[/C][C]0.564979[/C][/ROW]
[ROW][C]68[/C][C]0.380784[/C][C]0.761568[/C][C]0.619216[/C][/ROW]
[ROW][C]69[/C][C]0.494757[/C][C]0.989515[/C][C]0.505243[/C][/ROW]
[ROW][C]70[/C][C]0.44483[/C][C]0.88966[/C][C]0.55517[/C][/ROW]
[ROW][C]71[/C][C]0.395101[/C][C]0.790202[/C][C]0.604899[/C][/ROW]
[ROW][C]72[/C][C]0.342231[/C][C]0.684462[/C][C]0.657769[/C][/ROW]
[ROW][C]73[/C][C]0.321315[/C][C]0.64263[/C][C]0.678685[/C][/ROW]
[ROW][C]74[/C][C]0.285538[/C][C]0.571077[/C][C]0.714462[/C][/ROW]
[ROW][C]75[/C][C]0.246939[/C][C]0.493877[/C][C]0.753061[/C][/ROW]
[ROW][C]76[/C][C]0.205187[/C][C]0.410375[/C][C]0.794813[/C][/ROW]
[ROW][C]77[/C][C]0.410774[/C][C]0.821548[/C][C]0.589226[/C][/ROW]
[ROW][C]78[/C][C]0.406669[/C][C]0.813339[/C][C]0.593331[/C][/ROW]
[ROW][C]79[/C][C]0.342106[/C][C]0.684213[/C][C]0.657894[/C][/ROW]
[ROW][C]80[/C][C]0.280406[/C][C]0.560811[/C][C]0.719594[/C][/ROW]
[ROW][C]81[/C][C]0.829022[/C][C]0.341955[/C][C]0.170978[/C][/ROW]
[ROW][C]82[/C][C]0.816247[/C][C]0.367506[/C][C]0.183753[/C][/ROW]
[ROW][C]83[/C][C]0.828803[/C][C]0.342394[/C][C]0.171197[/C][/ROW]
[ROW][C]84[/C][C]0.783475[/C][C]0.433049[/C][C]0.216525[/C][/ROW]
[ROW][C]85[/C][C]0.891799[/C][C]0.216401[/C][C]0.108201[/C][/ROW]
[ROW][C]86[/C][C]0.868767[/C][C]0.262465[/C][C]0.131233[/C][/ROW]
[ROW][C]87[/C][C]0.835597[/C][C]0.328806[/C][C]0.164403[/C][/ROW]
[ROW][C]88[/C][C]0.783064[/C][C]0.433872[/C][C]0.216936[/C][/ROW]
[ROW][C]89[/C][C]0.68043[/C][C]0.639141[/C][C]0.31957[/C][/ROW]
[ROW][C]90[/C][C]0.626394[/C][C]0.747211[/C][C]0.373606[/C][/ROW]
[ROW][C]91[/C][C]0.992471[/C][C]0.0150586[/C][C]0.00752929[/C][/ROW]
[ROW][C]92[/C][C]0.970509[/C][C]0.0589811[/C][C]0.0294906[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268927&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268927&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
100.8911170.2177650.108883
110.8190430.3619130.180957
120.7666690.4666610.233331
130.7221560.5556880.277844
140.6786810.6426380.321319
150.6595910.6808180.340409
160.660290.679420.33971
170.6828210.6343580.317179
180.6342710.7314580.365729
190.5840330.8319350.415967
200.5465650.9068690.453435
210.4975760.9951520.502424
220.7287230.5425540.271277
230.6894950.6210090.310505
240.6457140.7085730.354286
250.5801390.8397230.419861
260.569940.860120.43006
270.650880.698240.34912
280.698860.6022810.30114
290.6803270.6393450.319673
300.6236020.7527960.376398
310.9206450.1587110.0793553
320.8953670.2092660.104633
330.8630780.2738430.136922
340.835940.3281210.16406
350.8089080.3821830.191092
360.8318190.3363620.168181
370.7999150.4001710.200085
380.89360.2127990.1064
390.8871210.2257580.112879
400.8626730.2746540.137327
410.8258750.348250.174125
420.8217930.3564140.178207
430.7868580.4262850.213142
440.7734870.4530270.226513
450.7948560.4102880.205144
460.7502920.4994170.249708
470.7112060.5775890.288794
480.6679170.6641660.332083
490.6388760.7222490.361124
500.6236120.7527770.376388
510.6545160.6909680.345484
520.7261050.5477910.273895
530.6775930.6448140.322407
540.630810.7383790.36919
550.5733130.8533740.426687
560.530220.9395590.46978
570.4880060.9760120.511994
580.4493670.8987340.550633
590.4282240.8564480.571776
600.4037930.8075870.596207
610.3702250.7404490.629775
620.5045770.9908460.495423
630.5100410.9799190.489959
640.4890810.9781620.510919
650.4395290.8790580.560471
660.4908490.9816980.509151
670.4350210.8700410.564979
680.3807840.7615680.619216
690.4947570.9895150.505243
700.444830.889660.55517
710.3951010.7902020.604899
720.3422310.6844620.657769
730.3213150.642630.678685
740.2855380.5710770.714462
750.2469390.4938770.753061
760.2051870.4103750.794813
770.4107740.8215480.589226
780.4066690.8133390.593331
790.3421060.6842130.657894
800.2804060.5608110.719594
810.8290220.3419550.170978
820.8162470.3675060.183753
830.8288030.3423940.171197
840.7834750.4330490.216525
850.8917990.2164010.108201
860.8687670.2624650.131233
870.8355970.3288060.164403
880.7830640.4338720.216936
890.680430.6391410.31957
900.6263940.7472110.373606
910.9924710.01505860.00752929
920.9705090.05898110.0294906






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0120482OK
10% type I error level20.0240964OK

\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 & 1 & 0.0120482 & OK \tabularnewline
10% type I error level & 2 & 0.0240964 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268927&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]1[/C][C]0.0120482[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0240964[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268927&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268927&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 level10.0120482OK
10% type I error level20.0240964OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
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')
}