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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 17:22:33 +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/t14186641917k6c5k5f5o9myh1.htm/, Retrieved Fri, 01 Nov 2024 00:01:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=268774, Retrieved Fri, 01 Nov 2024 00:01:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact98

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 16:39:07] [eee95947b6243a1febfcd5f41483d733]
-    D      [Multiple Regression] [] [2014-12-15 17:22:33] [ef562ec391a3ad5a7cbe41e167f467b9] [Current]
-    D        [Multiple Regression] [] [2014-12-15 17:24:15] [eee95947b6243a1febfcd5f41483d733]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
22	20	20	24	24	24	11,3
20	15	14	23	18	25	10,5
19	21	9	26	20	26	10,9
15	18	7	16	21	22	10,3
22	25	16	25	25	28	11,4
11	16	5	20	22	23	8,8
22	23	22	24	24	23	9,0
22	18	20	23	19	24	6,4
22	23	18	24	22	23	11,6
24	16	12	26	11	20	16,6
23	22	19	22	14	21	14,85
19	12	9	21	21	24	11,75
18	17	11	24	18	21	18,45
17	24	10	17	17	14	19,9
21	18	13	21	24	28	18,45
26	21	19	24	15	22	15
19	21	21	22	22	24	11,35
21	17	13	24	17	21	18,1
20	18	12	24	13	21	13,4
17	14	8	21	18	19	13,9
21	20	17	23	21	28	15,25
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
22	21	21	21	23	22	18,2
20	8	10	25	9	21	13,6
16	17	15	23	22	17	14,1
19	16	12	27	12	25	14,9
23	22	21	27	18	19	16,25
15	20	9	23	22	25	15,65
21	8	14	25	19	25	14,6
20	13	12	25	24	24	19,2
22	22	11	27	25	26	13,2
18	21	12	21	24	20	15,65
20	21	23	26	26	26	7,65
18	18	11	21	21	21	15,2
23	20	13	26	24	23	11,85
19	18	13	28	28	28	11,4
16	18	13	21	23	14	19,9
4	11	7	15	20	25	15,15
22	20	17	22	20	24	16,85
20	21	12	24	25	23	12,6
20	18	16	26	24	24	12,35
21	16	16	24	18	23	16,65
19	7	10	22	8	21	13,95
27	21	28	28	27	25	15,7
20	20	19	28	20	26	15,35
21	17	17	24	23	23	15,1
23	19	16	24	24	21	17,75
20	20	17	26	22	23	16,65
11	8	7	18	12	20	12,9
16	12	9	22	14	18	12,8
19	16	17	28	21	24	14,8
24	22	12	26	24	23	12,0
25	25	18	28	23	24	6,3
21	19	16	26	20	24	9,3
27	26	21	28	25	28	10,0
22	19	17	24	21	26	10,8
21	20	12	26	25	25	11,5
8	15	6	23	23	24	8,3
22	19	13	21	19	20	11,7
23	19	12	23	24	23	10,4
19	18	10	24	28	24	11,8
24	22	22	23	22	22	11,3
22	19	15	27	19	24	12,5
15	16	10	25	24	26	7,6
19	16	18	21	16	23	9,2
20	15	16	23	18	18	12,3
18	14	5	27	25	26	12,6
16	16	10	25	17	25	13,0
24	26	16	24	24	18	13,2
23	20	16	28	15	20	19,1
20	18	14	19	21	25	13,35
18	13	13	19	9	19	18,4
22	19	19	26	21	21	16,15
21	22	16	22	24	25	18,4
22	21	12	22	23	23	16,35
17	14	10	20	13	15	17,65
20	21	11	18	22	20	14,35
19	16	15	18	21	20	14,75
24	25	11	28	23	28	9,9
13	9	7	22	15	22	16,85
22	22	11	20	25	26	15,6
21	10	12	24	12	22	17,1
11	7	7	19	18	25	19,1
19	14	13	25	23	21	7,6
19	18	11	22	27	23	14,75
23	20	15	24	22	22	13,6
22	20	18	24	27	23	11,9
23	19	14	25	24	26	16,35
21	20	17	28	24	26	17,75
16	19	16	23	23	24	19,3
23	20	16	26	19	18	17,1
20	19	12	24	20	23	19,05
24	23	17	23	18	25	18,55
25	16	11	22	20	20	19,1
27	26	18	22	26	21	13,35
16	13	14	26	12	19	17,6
23	21	19	27	20	21	16,1
20	23	8	22	23	24	11,95
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 time6 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268774&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268774&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268774&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'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TOTAAL[t] = + 23.9804 + 0.149176I1[t] -0.0897385I2[t] + 0.000776772I3[t] -0.141206E1[t] -0.108164E2[t] -0.253893E3[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOTAAL[t] =  +  23.9804 +  0.149176I1[t] -0.0897385I2[t] +  0.000776772I3[t] -0.141206E1[t] -0.108164E2[t] -0.253893E3[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268774&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOTAAL[t] =  +  23.9804 +  0.149176I1[t] -0.0897385I2[t] +  0.000776772I3[t] -0.141206E1[t] -0.108164E2[t] -0.253893E3[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268774&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268774&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] = + 23.9804 + 0.149176I1[t] -0.0897385I2[t] + 0.000776772I3[t] -0.141206E1[t] -0.108164E2[t] -0.253893E3[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)23.98043.148527.6161.82545e-119.12723e-12
I10.1491760.1290351.1560.2505140.125257
I2-0.08973850.10989-0.81660.4161650.208083
I30.0007767720.09725690.0079870.9936440.496822
E1-0.1412060.136443-1.0350.3033120.151656
E2-0.1081640.0920957-1.1740.2431090.121555
E3-0.2538930.123845-2.050.04308220.0215411

\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) & 23.9804 & 3.14852 & 7.616 & 1.82545e-11 & 9.12723e-12 \tabularnewline
I1 & 0.149176 & 0.129035 & 1.156 & 0.250514 & 0.125257 \tabularnewline
I2 & -0.0897385 & 0.10989 & -0.8166 & 0.416165 & 0.208083 \tabularnewline
I3 & 0.000776772 & 0.0972569 & 0.007987 & 0.993644 & 0.496822 \tabularnewline
E1 & -0.141206 & 0.136443 & -1.035 & 0.303312 & 0.151656 \tabularnewline
E2 & -0.108164 & 0.0920957 & -1.174 & 0.243109 & 0.121555 \tabularnewline
E3 & -0.253893 & 0.123845 & -2.05 & 0.0430822 & 0.0215411 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268774&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]23.9804[/C][C]3.14852[/C][C]7.616[/C][C]1.82545e-11[/C][C]9.12723e-12[/C][/ROW]
[ROW][C]I1[/C][C]0.149176[/C][C]0.129035[/C][C]1.156[/C][C]0.250514[/C][C]0.125257[/C][/ROW]
[ROW][C]I2[/C][C]-0.0897385[/C][C]0.10989[/C][C]-0.8166[/C][C]0.416165[/C][C]0.208083[/C][/ROW]
[ROW][C]I3[/C][C]0.000776772[/C][C]0.0972569[/C][C]0.007987[/C][C]0.993644[/C][C]0.496822[/C][/ROW]
[ROW][C]E1[/C][C]-0.141206[/C][C]0.136443[/C][C]-1.035[/C][C]0.303312[/C][C]0.151656[/C][/ROW]
[ROW][C]E2[/C][C]-0.108164[/C][C]0.0920957[/C][C]-1.174[/C][C]0.243109[/C][C]0.121555[/C][/ROW]
[ROW][C]E3[/C][C]-0.253893[/C][C]0.123845[/C][C]-2.05[/C][C]0.0430822[/C][C]0.0215411[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268774&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268774&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)23.98043.148527.6161.82545e-119.12723e-12
I10.1491760.1290351.1560.2505140.125257
I2-0.08973850.10989-0.81660.4161650.208083
I30.0007767720.09725690.0079870.9936440.496822
E1-0.1412060.136443-1.0350.3033120.151656
E2-0.1081640.0920957-1.1740.2431090.121555
E3-0.2538930.123845-2.050.04308220.0215411






Multiple Linear Regression - Regression Statistics
Multiple R0.393655
R-squared0.154965
Adjusted R-squared0.10215
F-TEST (value)2.93412
F-TEST (DF numerator)6
F-TEST (DF denominator)96
p-value0.0113347
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.16605
Sum Squared Residuals962.291

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.393655 \tabularnewline
R-squared & 0.154965 \tabularnewline
Adjusted R-squared & 0.10215 \tabularnewline
F-TEST (value) & 2.93412 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 96 \tabularnewline
p-value & 0.0113347 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.16605 \tabularnewline
Sum Squared Residuals & 962.291 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268774&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.393655[/C][/ROW]
[ROW][C]R-squared[/C][C]0.154965[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.10215[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.93412[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]96[/C][/ROW]
[ROW][C]p-value[/C][C]0.0113347[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.16605[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]962.291[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268774&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268774&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 R0.393655
R-squared0.154965
Adjusted R-squared0.10215
F-TEST (value)2.93412
F-TEST (DF numerator)6
F-TEST (DF denominator)96
p-value0.0113347
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.16605
Sum Squared Residuals962.291






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
111.313.4047-2.10474
210.514.0867-3.58671
310.912.5014-1.60138
410.314.4918-4.19181
511.411.688-0.287994
68.813.1461-4.34614
7913.391-4.39097
86.414.2662-7.86624
911.613.6042-2.00419
1016.616.19510.404877
1114.8515.4994-0.649392
1211.7514.4147-2.66468
1318.4514.48093.96908
1419.916.57673.32335
1518.4512.83765.61236
161515.3922-0.39219
1711.3513.367-2.01699
1818.115.03823.06183
1913.415.2311-1.83113
2013.915.53-1.63003
2115.2512.70342.54664
2216.115.0821.01801
2317.3513.95713.39287
2413.1513.1993-0.0492666
2512.1513.4314-1.28135
2618.214.35533.84466
2713.616.4184-2.81841
2814.114.9098-0.809796
2914.913.93040.969593
3016.2514.871.37996
3115.6512.45563.1944
3214.614.47350.126517
3319.213.58715.61287
3413.212.17871.0213
3515.6514.15131.49873
367.6512.0124-4.36245
3715.214.49030.709693
3811.8513.52-1.66996
3911.411.11820.281807
4019.915.75444.14557
4115.1512.96672.18327
4216.8514.11752.73253
4312.613.1562-0.556159
4412.3513.0003-0.650341
4516.6514.51432.13572
4613.9516.8908-2.94076
4715.712.92392.77612
4815.3512.46572.88435
4915.113.88451.2155
5017.7514.40223.34778
5116.6513.29193.35814
5212.915.9913-3.09134
5312.816.1065-3.30645
5414.813.07351.7265
551213.4889-1.48888
566.312.9454-6.64536
579.313.4924-4.19244
581011.9244-1.9244
5910.813.3089-2.50885
6011.512.6049-1.10488
618.312.0035-3.70346
6211.715.469-3.76905
6310.414.0325-3.63254
6411.812.6963-0.896259
6511.314.3905-3.09049
6612.513.6078-1.10779
677.612.0627-4.4627
689.214.8574-5.65743
6912.315.8655-3.56552
7012.612.29520.304758
711313.2229-0.222915
7213.214.6849-1.48491
7319.114.9754.12497
7413.3514.0578-0.707829
7518.417.02871.37128
7616.1514.29751.85254
7718.413.10155.29851
7816.3513.95332.39675
7917.6517.22920.420814
8014.3515.0888-0.73879
8114.7515.4996-0.749578
829.911.7752-1.87517
8316.8514.80292.04715
8415.613.16712.43286
8517.115.95251.14751
8619.114.02145.07858
877.614.2188-6.61884
8814.7513.34151.40849
8913.614.2741-0.674143
9011.913.3326-1.43258
9116.3512.993.36
9217.7512.18065.56938
9319.312.84576.45432
9417.115.33261.76743
9519.0513.87655.17354
9618.5513.96784.58216
9719.115.93493.16513
9813.3514.4384-1.08839
9917.615.41822.18179
10016.114.23411.86589
10111.9513.2184-1.26842
1027.711.6491-3.94909
10314.615.4163-0.816292

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 11.3 & 13.4047 & -2.10474 \tabularnewline
2 & 10.5 & 14.0867 & -3.58671 \tabularnewline
3 & 10.9 & 12.5014 & -1.60138 \tabularnewline
4 & 10.3 & 14.4918 & -4.19181 \tabularnewline
5 & 11.4 & 11.688 & -0.287994 \tabularnewline
6 & 8.8 & 13.1461 & -4.34614 \tabularnewline
7 & 9 & 13.391 & -4.39097 \tabularnewline
8 & 6.4 & 14.2662 & -7.86624 \tabularnewline
9 & 11.6 & 13.6042 & -2.00419 \tabularnewline
10 & 16.6 & 16.1951 & 0.404877 \tabularnewline
11 & 14.85 & 15.4994 & -0.649392 \tabularnewline
12 & 11.75 & 14.4147 & -2.66468 \tabularnewline
13 & 18.45 & 14.4809 & 3.96908 \tabularnewline
14 & 19.9 & 16.5767 & 3.32335 \tabularnewline
15 & 18.45 & 12.8376 & 5.61236 \tabularnewline
16 & 15 & 15.3922 & -0.39219 \tabularnewline
17 & 11.35 & 13.367 & -2.01699 \tabularnewline
18 & 18.1 & 15.0382 & 3.06183 \tabularnewline
19 & 13.4 & 15.2311 & -1.83113 \tabularnewline
20 & 13.9 & 15.53 & -1.63003 \tabularnewline
21 & 15.25 & 12.7034 & 2.54664 \tabularnewline
22 & 16.1 & 15.082 & 1.01801 \tabularnewline
23 & 17.35 & 13.9571 & 3.39287 \tabularnewline
24 & 13.15 & 13.1993 & -0.0492666 \tabularnewline
25 & 12.15 & 13.4314 & -1.28135 \tabularnewline
26 & 18.2 & 14.3553 & 3.84466 \tabularnewline
27 & 13.6 & 16.4184 & -2.81841 \tabularnewline
28 & 14.1 & 14.9098 & -0.809796 \tabularnewline
29 & 14.9 & 13.9304 & 0.969593 \tabularnewline
30 & 16.25 & 14.87 & 1.37996 \tabularnewline
31 & 15.65 & 12.4556 & 3.1944 \tabularnewline
32 & 14.6 & 14.4735 & 0.126517 \tabularnewline
33 & 19.2 & 13.5871 & 5.61287 \tabularnewline
34 & 13.2 & 12.1787 & 1.0213 \tabularnewline
35 & 15.65 & 14.1513 & 1.49873 \tabularnewline
36 & 7.65 & 12.0124 & -4.36245 \tabularnewline
37 & 15.2 & 14.4903 & 0.709693 \tabularnewline
38 & 11.85 & 13.52 & -1.66996 \tabularnewline
39 & 11.4 & 11.1182 & 0.281807 \tabularnewline
40 & 19.9 & 15.7544 & 4.14557 \tabularnewline
41 & 15.15 & 12.9667 & 2.18327 \tabularnewline
42 & 16.85 & 14.1175 & 2.73253 \tabularnewline
43 & 12.6 & 13.1562 & -0.556159 \tabularnewline
44 & 12.35 & 13.0003 & -0.650341 \tabularnewline
45 & 16.65 & 14.5143 & 2.13572 \tabularnewline
46 & 13.95 & 16.8908 & -2.94076 \tabularnewline
47 & 15.7 & 12.9239 & 2.77612 \tabularnewline
48 & 15.35 & 12.4657 & 2.88435 \tabularnewline
49 & 15.1 & 13.8845 & 1.2155 \tabularnewline
50 & 17.75 & 14.4022 & 3.34778 \tabularnewline
51 & 16.65 & 13.2919 & 3.35814 \tabularnewline
52 & 12.9 & 15.9913 & -3.09134 \tabularnewline
53 & 12.8 & 16.1065 & -3.30645 \tabularnewline
54 & 14.8 & 13.0735 & 1.7265 \tabularnewline
55 & 12 & 13.4889 & -1.48888 \tabularnewline
56 & 6.3 & 12.9454 & -6.64536 \tabularnewline
57 & 9.3 & 13.4924 & -4.19244 \tabularnewline
58 & 10 & 11.9244 & -1.9244 \tabularnewline
59 & 10.8 & 13.3089 & -2.50885 \tabularnewline
60 & 11.5 & 12.6049 & -1.10488 \tabularnewline
61 & 8.3 & 12.0035 & -3.70346 \tabularnewline
62 & 11.7 & 15.469 & -3.76905 \tabularnewline
63 & 10.4 & 14.0325 & -3.63254 \tabularnewline
64 & 11.8 & 12.6963 & -0.896259 \tabularnewline
65 & 11.3 & 14.3905 & -3.09049 \tabularnewline
66 & 12.5 & 13.6078 & -1.10779 \tabularnewline
67 & 7.6 & 12.0627 & -4.4627 \tabularnewline
68 & 9.2 & 14.8574 & -5.65743 \tabularnewline
69 & 12.3 & 15.8655 & -3.56552 \tabularnewline
70 & 12.6 & 12.2952 & 0.304758 \tabularnewline
71 & 13 & 13.2229 & -0.222915 \tabularnewline
72 & 13.2 & 14.6849 & -1.48491 \tabularnewline
73 & 19.1 & 14.975 & 4.12497 \tabularnewline
74 & 13.35 & 14.0578 & -0.707829 \tabularnewline
75 & 18.4 & 17.0287 & 1.37128 \tabularnewline
76 & 16.15 & 14.2975 & 1.85254 \tabularnewline
77 & 18.4 & 13.1015 & 5.29851 \tabularnewline
78 & 16.35 & 13.9533 & 2.39675 \tabularnewline
79 & 17.65 & 17.2292 & 0.420814 \tabularnewline
80 & 14.35 & 15.0888 & -0.73879 \tabularnewline
81 & 14.75 & 15.4996 & -0.749578 \tabularnewline
82 & 9.9 & 11.7752 & -1.87517 \tabularnewline
83 & 16.85 & 14.8029 & 2.04715 \tabularnewline
84 & 15.6 & 13.1671 & 2.43286 \tabularnewline
85 & 17.1 & 15.9525 & 1.14751 \tabularnewline
86 & 19.1 & 14.0214 & 5.07858 \tabularnewline
87 & 7.6 & 14.2188 & -6.61884 \tabularnewline
88 & 14.75 & 13.3415 & 1.40849 \tabularnewline
89 & 13.6 & 14.2741 & -0.674143 \tabularnewline
90 & 11.9 & 13.3326 & -1.43258 \tabularnewline
91 & 16.35 & 12.99 & 3.36 \tabularnewline
92 & 17.75 & 12.1806 & 5.56938 \tabularnewline
93 & 19.3 & 12.8457 & 6.45432 \tabularnewline
94 & 17.1 & 15.3326 & 1.76743 \tabularnewline
95 & 19.05 & 13.8765 & 5.17354 \tabularnewline
96 & 18.55 & 13.9678 & 4.58216 \tabularnewline
97 & 19.1 & 15.9349 & 3.16513 \tabularnewline
98 & 13.35 & 14.4384 & -1.08839 \tabularnewline
99 & 17.6 & 15.4182 & 2.18179 \tabularnewline
100 & 16.1 & 14.2341 & 1.86589 \tabularnewline
101 & 11.95 & 13.2184 & -1.26842 \tabularnewline
102 & 7.7 & 11.6491 & -3.94909 \tabularnewline
103 & 14.6 & 15.4163 & -0.816292 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268774&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]13.4047[/C][C]-2.10474[/C][/ROW]
[ROW][C]2[/C][C]10.5[/C][C]14.0867[/C][C]-3.58671[/C][/ROW]
[ROW][C]3[/C][C]10.9[/C][C]12.5014[/C][C]-1.60138[/C][/ROW]
[ROW][C]4[/C][C]10.3[/C][C]14.4918[/C][C]-4.19181[/C][/ROW]
[ROW][C]5[/C][C]11.4[/C][C]11.688[/C][C]-0.287994[/C][/ROW]
[ROW][C]6[/C][C]8.8[/C][C]13.1461[/C][C]-4.34614[/C][/ROW]
[ROW][C]7[/C][C]9[/C][C]13.391[/C][C]-4.39097[/C][/ROW]
[ROW][C]8[/C][C]6.4[/C][C]14.2662[/C][C]-7.86624[/C][/ROW]
[ROW][C]9[/C][C]11.6[/C][C]13.6042[/C][C]-2.00419[/C][/ROW]
[ROW][C]10[/C][C]16.6[/C][C]16.1951[/C][C]0.404877[/C][/ROW]
[ROW][C]11[/C][C]14.85[/C][C]15.4994[/C][C]-0.649392[/C][/ROW]
[ROW][C]12[/C][C]11.75[/C][C]14.4147[/C][C]-2.66468[/C][/ROW]
[ROW][C]13[/C][C]18.45[/C][C]14.4809[/C][C]3.96908[/C][/ROW]
[ROW][C]14[/C][C]19.9[/C][C]16.5767[/C][C]3.32335[/C][/ROW]
[ROW][C]15[/C][C]18.45[/C][C]12.8376[/C][C]5.61236[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.3922[/C][C]-0.39219[/C][/ROW]
[ROW][C]17[/C][C]11.35[/C][C]13.367[/C][C]-2.01699[/C][/ROW]
[ROW][C]18[/C][C]18.1[/C][C]15.0382[/C][C]3.06183[/C][/ROW]
[ROW][C]19[/C][C]13.4[/C][C]15.2311[/C][C]-1.83113[/C][/ROW]
[ROW][C]20[/C][C]13.9[/C][C]15.53[/C][C]-1.63003[/C][/ROW]
[ROW][C]21[/C][C]15.25[/C][C]12.7034[/C][C]2.54664[/C][/ROW]
[ROW][C]22[/C][C]16.1[/C][C]15.082[/C][C]1.01801[/C][/ROW]
[ROW][C]23[/C][C]17.35[/C][C]13.9571[/C][C]3.39287[/C][/ROW]
[ROW][C]24[/C][C]13.15[/C][C]13.1993[/C][C]-0.0492666[/C][/ROW]
[ROW][C]25[/C][C]12.15[/C][C]13.4314[/C][C]-1.28135[/C][/ROW]
[ROW][C]26[/C][C]18.2[/C][C]14.3553[/C][C]3.84466[/C][/ROW]
[ROW][C]27[/C][C]13.6[/C][C]16.4184[/C][C]-2.81841[/C][/ROW]
[ROW][C]28[/C][C]14.1[/C][C]14.9098[/C][C]-0.809796[/C][/ROW]
[ROW][C]29[/C][C]14.9[/C][C]13.9304[/C][C]0.969593[/C][/ROW]
[ROW][C]30[/C][C]16.25[/C][C]14.87[/C][C]1.37996[/C][/ROW]
[ROW][C]31[/C][C]15.65[/C][C]12.4556[/C][C]3.1944[/C][/ROW]
[ROW][C]32[/C][C]14.6[/C][C]14.4735[/C][C]0.126517[/C][/ROW]
[ROW][C]33[/C][C]19.2[/C][C]13.5871[/C][C]5.61287[/C][/ROW]
[ROW][C]34[/C][C]13.2[/C][C]12.1787[/C][C]1.0213[/C][/ROW]
[ROW][C]35[/C][C]15.65[/C][C]14.1513[/C][C]1.49873[/C][/ROW]
[ROW][C]36[/C][C]7.65[/C][C]12.0124[/C][C]-4.36245[/C][/ROW]
[ROW][C]37[/C][C]15.2[/C][C]14.4903[/C][C]0.709693[/C][/ROW]
[ROW][C]38[/C][C]11.85[/C][C]13.52[/C][C]-1.66996[/C][/ROW]
[ROW][C]39[/C][C]11.4[/C][C]11.1182[/C][C]0.281807[/C][/ROW]
[ROW][C]40[/C][C]19.9[/C][C]15.7544[/C][C]4.14557[/C][/ROW]
[ROW][C]41[/C][C]15.15[/C][C]12.9667[/C][C]2.18327[/C][/ROW]
[ROW][C]42[/C][C]16.85[/C][C]14.1175[/C][C]2.73253[/C][/ROW]
[ROW][C]43[/C][C]12.6[/C][C]13.1562[/C][C]-0.556159[/C][/ROW]
[ROW][C]44[/C][C]12.35[/C][C]13.0003[/C][C]-0.650341[/C][/ROW]
[ROW][C]45[/C][C]16.65[/C][C]14.5143[/C][C]2.13572[/C][/ROW]
[ROW][C]46[/C][C]13.95[/C][C]16.8908[/C][C]-2.94076[/C][/ROW]
[ROW][C]47[/C][C]15.7[/C][C]12.9239[/C][C]2.77612[/C][/ROW]
[ROW][C]48[/C][C]15.35[/C][C]12.4657[/C][C]2.88435[/C][/ROW]
[ROW][C]49[/C][C]15.1[/C][C]13.8845[/C][C]1.2155[/C][/ROW]
[ROW][C]50[/C][C]17.75[/C][C]14.4022[/C][C]3.34778[/C][/ROW]
[ROW][C]51[/C][C]16.65[/C][C]13.2919[/C][C]3.35814[/C][/ROW]
[ROW][C]52[/C][C]12.9[/C][C]15.9913[/C][C]-3.09134[/C][/ROW]
[ROW][C]53[/C][C]12.8[/C][C]16.1065[/C][C]-3.30645[/C][/ROW]
[ROW][C]54[/C][C]14.8[/C][C]13.0735[/C][C]1.7265[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]13.4889[/C][C]-1.48888[/C][/ROW]
[ROW][C]56[/C][C]6.3[/C][C]12.9454[/C][C]-6.64536[/C][/ROW]
[ROW][C]57[/C][C]9.3[/C][C]13.4924[/C][C]-4.19244[/C][/ROW]
[ROW][C]58[/C][C]10[/C][C]11.9244[/C][C]-1.9244[/C][/ROW]
[ROW][C]59[/C][C]10.8[/C][C]13.3089[/C][C]-2.50885[/C][/ROW]
[ROW][C]60[/C][C]11.5[/C][C]12.6049[/C][C]-1.10488[/C][/ROW]
[ROW][C]61[/C][C]8.3[/C][C]12.0035[/C][C]-3.70346[/C][/ROW]
[ROW][C]62[/C][C]11.7[/C][C]15.469[/C][C]-3.76905[/C][/ROW]
[ROW][C]63[/C][C]10.4[/C][C]14.0325[/C][C]-3.63254[/C][/ROW]
[ROW][C]64[/C][C]11.8[/C][C]12.6963[/C][C]-0.896259[/C][/ROW]
[ROW][C]65[/C][C]11.3[/C][C]14.3905[/C][C]-3.09049[/C][/ROW]
[ROW][C]66[/C][C]12.5[/C][C]13.6078[/C][C]-1.10779[/C][/ROW]
[ROW][C]67[/C][C]7.6[/C][C]12.0627[/C][C]-4.4627[/C][/ROW]
[ROW][C]68[/C][C]9.2[/C][C]14.8574[/C][C]-5.65743[/C][/ROW]
[ROW][C]69[/C][C]12.3[/C][C]15.8655[/C][C]-3.56552[/C][/ROW]
[ROW][C]70[/C][C]12.6[/C][C]12.2952[/C][C]0.304758[/C][/ROW]
[ROW][C]71[/C][C]13[/C][C]13.2229[/C][C]-0.222915[/C][/ROW]
[ROW][C]72[/C][C]13.2[/C][C]14.6849[/C][C]-1.48491[/C][/ROW]
[ROW][C]73[/C][C]19.1[/C][C]14.975[/C][C]4.12497[/C][/ROW]
[ROW][C]74[/C][C]13.35[/C][C]14.0578[/C][C]-0.707829[/C][/ROW]
[ROW][C]75[/C][C]18.4[/C][C]17.0287[/C][C]1.37128[/C][/ROW]
[ROW][C]76[/C][C]16.15[/C][C]14.2975[/C][C]1.85254[/C][/ROW]
[ROW][C]77[/C][C]18.4[/C][C]13.1015[/C][C]5.29851[/C][/ROW]
[ROW][C]78[/C][C]16.35[/C][C]13.9533[/C][C]2.39675[/C][/ROW]
[ROW][C]79[/C][C]17.65[/C][C]17.2292[/C][C]0.420814[/C][/ROW]
[ROW][C]80[/C][C]14.35[/C][C]15.0888[/C][C]-0.73879[/C][/ROW]
[ROW][C]81[/C][C]14.75[/C][C]15.4996[/C][C]-0.749578[/C][/ROW]
[ROW][C]82[/C][C]9.9[/C][C]11.7752[/C][C]-1.87517[/C][/ROW]
[ROW][C]83[/C][C]16.85[/C][C]14.8029[/C][C]2.04715[/C][/ROW]
[ROW][C]84[/C][C]15.6[/C][C]13.1671[/C][C]2.43286[/C][/ROW]
[ROW][C]85[/C][C]17.1[/C][C]15.9525[/C][C]1.14751[/C][/ROW]
[ROW][C]86[/C][C]19.1[/C][C]14.0214[/C][C]5.07858[/C][/ROW]
[ROW][C]87[/C][C]7.6[/C][C]14.2188[/C][C]-6.61884[/C][/ROW]
[ROW][C]88[/C][C]14.75[/C][C]13.3415[/C][C]1.40849[/C][/ROW]
[ROW][C]89[/C][C]13.6[/C][C]14.2741[/C][C]-0.674143[/C][/ROW]
[ROW][C]90[/C][C]11.9[/C][C]13.3326[/C][C]-1.43258[/C][/ROW]
[ROW][C]91[/C][C]16.35[/C][C]12.99[/C][C]3.36[/C][/ROW]
[ROW][C]92[/C][C]17.75[/C][C]12.1806[/C][C]5.56938[/C][/ROW]
[ROW][C]93[/C][C]19.3[/C][C]12.8457[/C][C]6.45432[/C][/ROW]
[ROW][C]94[/C][C]17.1[/C][C]15.3326[/C][C]1.76743[/C][/ROW]
[ROW][C]95[/C][C]19.05[/C][C]13.8765[/C][C]5.17354[/C][/ROW]
[ROW][C]96[/C][C]18.55[/C][C]13.9678[/C][C]4.58216[/C][/ROW]
[ROW][C]97[/C][C]19.1[/C][C]15.9349[/C][C]3.16513[/C][/ROW]
[ROW][C]98[/C][C]13.35[/C][C]14.4384[/C][C]-1.08839[/C][/ROW]
[ROW][C]99[/C][C]17.6[/C][C]15.4182[/C][C]2.18179[/C][/ROW]
[ROW][C]100[/C][C]16.1[/C][C]14.2341[/C][C]1.86589[/C][/ROW]
[ROW][C]101[/C][C]11.95[/C][C]13.2184[/C][C]-1.26842[/C][/ROW]
[ROW][C]102[/C][C]7.7[/C][C]11.6491[/C][C]-3.94909[/C][/ROW]
[ROW][C]103[/C][C]14.6[/C][C]15.4163[/C][C]-0.816292[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268774&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268774&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
111.313.4047-2.10474
210.514.0867-3.58671
310.912.5014-1.60138
410.314.4918-4.19181
511.411.688-0.287994
68.813.1461-4.34614
7913.391-4.39097
86.414.2662-7.86624
911.613.6042-2.00419
1016.616.19510.404877
1114.8515.4994-0.649392
1211.7514.4147-2.66468
1318.4514.48093.96908
1419.916.57673.32335
1518.4512.83765.61236
161515.3922-0.39219
1711.3513.367-2.01699
1818.115.03823.06183
1913.415.2311-1.83113
2013.915.53-1.63003
2115.2512.70342.54664
2216.115.0821.01801
2317.3513.95713.39287
2413.1513.1993-0.0492666
2512.1513.4314-1.28135
2618.214.35533.84466
2713.616.4184-2.81841
2814.114.9098-0.809796
2914.913.93040.969593
3016.2514.871.37996
3115.6512.45563.1944
3214.614.47350.126517
3319.213.58715.61287
3413.212.17871.0213
3515.6514.15131.49873
367.6512.0124-4.36245
3715.214.49030.709693
3811.8513.52-1.66996
3911.411.11820.281807
4019.915.75444.14557
4115.1512.96672.18327
4216.8514.11752.73253
4312.613.1562-0.556159
4412.3513.0003-0.650341
4516.6514.51432.13572
4613.9516.8908-2.94076
4715.712.92392.77612
4815.3512.46572.88435
4915.113.88451.2155
5017.7514.40223.34778
5116.6513.29193.35814
5212.915.9913-3.09134
5312.816.1065-3.30645
5414.813.07351.7265
551213.4889-1.48888
566.312.9454-6.64536
579.313.4924-4.19244
581011.9244-1.9244
5910.813.3089-2.50885
6011.512.6049-1.10488
618.312.0035-3.70346
6211.715.469-3.76905
6310.414.0325-3.63254
6411.812.6963-0.896259
6511.314.3905-3.09049
6612.513.6078-1.10779
677.612.0627-4.4627
689.214.8574-5.65743
6912.315.8655-3.56552
7012.612.29520.304758
711313.2229-0.222915
7213.214.6849-1.48491
7319.114.9754.12497
7413.3514.0578-0.707829
7518.417.02871.37128
7616.1514.29751.85254
7718.413.10155.29851
7816.3513.95332.39675
7917.6517.22920.420814
8014.3515.0888-0.73879
8114.7515.4996-0.749578
829.911.7752-1.87517
8316.8514.80292.04715
8415.613.16712.43286
8517.115.95251.14751
8619.114.02145.07858
877.614.2188-6.61884
8814.7513.34151.40849
8913.614.2741-0.674143
9011.913.3326-1.43258
9116.3512.993.36
9217.7512.18065.56938
9319.312.84576.45432
9417.115.33261.76743
9519.0513.87655.17354
9618.5513.96784.58216
9719.115.93493.16513
9813.3514.4384-1.08839
9917.615.41822.18179
10016.114.23411.86589
10111.9513.2184-1.26842
1027.711.6491-3.94909
10314.615.4163-0.816292






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.06360110.1272020.936399
110.2612220.5224430.738778
120.1596490.3192970.840351
130.4283740.8567470.571626
140.319240.638480.68076
150.8249410.3501170.175059
160.7566240.4867530.243376
170.7856830.4286340.214317
180.7856450.4287110.214355
190.7204660.5590680.279534
200.6579350.684130.342065
210.7284680.5430640.271532
220.6943110.6113770.305689
230.7035370.5929260.296463
240.635930.7281410.36407
250.5718670.8562650.428133
260.6471430.7057150.352857
270.5904010.8191980.409599
280.5521050.8957890.447895
290.5403550.9192910.459645
300.484270.968540.51573
310.5129850.9740290.487015
320.4736110.9472230.526389
330.5713650.8572690.428635
340.5360950.9278090.463905
350.4769140.9538290.523086
360.495080.9901590.50492
370.4329960.8659910.567004
380.4408020.8816030.559198
390.3795680.7591370.620432
400.4242260.8484520.575774
410.4591990.9183970.540801
420.4387380.8774770.561262
430.3851730.7703450.614827
440.3296220.6592450.670378
450.3004630.6009270.699537
460.2958640.5917280.704136
470.2795580.5591170.720442
480.2736830.5473660.726317
490.2303310.4606620.769669
500.2276340.4552690.772366
510.2319510.4639030.768049
520.2240050.448010.775995
530.226510.453020.77349
540.1981870.3963740.801813
550.1767910.3535810.823209
560.3400860.6801720.659914
570.3834160.7668330.616584
580.3559230.7118460.644077
590.3526080.7052160.647392
600.3050710.6101430.694929
610.307220.614440.69278
620.3329890.6659780.667011
630.3500360.7000720.649964
640.2962190.5924390.703781
650.2983330.5966660.701667
660.2666190.5332380.733381
670.3422220.6844430.657778
680.6341290.7317420.365871
690.6725050.654990.327495
700.6135060.7729880.386494
710.5957110.8085770.404289
720.53410.9318010.4659
730.5416150.916770.458385
740.5679020.8641970.432098
750.5561810.8876380.443819
760.4925960.9851920.507404
770.5144260.9711470.485574
780.4746340.9492680.525366
790.4055770.8111550.594423
800.3336170.6672340.666383
810.3134770.6269540.686523
820.2940380.5880770.705962
830.2363680.4727360.763632
840.1828230.3656460.817177
850.2001090.4002190.799891
860.1659250.3318510.834075
870.5079320.9841360.492068
880.4171640.8343290.582836
890.3573450.7146890.642655
900.3857590.7715170.614241
910.324680.6493590.67532
920.232230.4644610.76777
930.5713020.8573960.428698

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.0636011 & 0.127202 & 0.936399 \tabularnewline
11 & 0.261222 & 0.522443 & 0.738778 \tabularnewline
12 & 0.159649 & 0.319297 & 0.840351 \tabularnewline
13 & 0.428374 & 0.856747 & 0.571626 \tabularnewline
14 & 0.31924 & 0.63848 & 0.68076 \tabularnewline
15 & 0.824941 & 0.350117 & 0.175059 \tabularnewline
16 & 0.756624 & 0.486753 & 0.243376 \tabularnewline
17 & 0.785683 & 0.428634 & 0.214317 \tabularnewline
18 & 0.785645 & 0.428711 & 0.214355 \tabularnewline
19 & 0.720466 & 0.559068 & 0.279534 \tabularnewline
20 & 0.657935 & 0.68413 & 0.342065 \tabularnewline
21 & 0.728468 & 0.543064 & 0.271532 \tabularnewline
22 & 0.694311 & 0.611377 & 0.305689 \tabularnewline
23 & 0.703537 & 0.592926 & 0.296463 \tabularnewline
24 & 0.63593 & 0.728141 & 0.36407 \tabularnewline
25 & 0.571867 & 0.856265 & 0.428133 \tabularnewline
26 & 0.647143 & 0.705715 & 0.352857 \tabularnewline
27 & 0.590401 & 0.819198 & 0.409599 \tabularnewline
28 & 0.552105 & 0.895789 & 0.447895 \tabularnewline
29 & 0.540355 & 0.919291 & 0.459645 \tabularnewline
30 & 0.48427 & 0.96854 & 0.51573 \tabularnewline
31 & 0.512985 & 0.974029 & 0.487015 \tabularnewline
32 & 0.473611 & 0.947223 & 0.526389 \tabularnewline
33 & 0.571365 & 0.857269 & 0.428635 \tabularnewline
34 & 0.536095 & 0.927809 & 0.463905 \tabularnewline
35 & 0.476914 & 0.953829 & 0.523086 \tabularnewline
36 & 0.49508 & 0.990159 & 0.50492 \tabularnewline
37 & 0.432996 & 0.865991 & 0.567004 \tabularnewline
38 & 0.440802 & 0.881603 & 0.559198 \tabularnewline
39 & 0.379568 & 0.759137 & 0.620432 \tabularnewline
40 & 0.424226 & 0.848452 & 0.575774 \tabularnewline
41 & 0.459199 & 0.918397 & 0.540801 \tabularnewline
42 & 0.438738 & 0.877477 & 0.561262 \tabularnewline
43 & 0.385173 & 0.770345 & 0.614827 \tabularnewline
44 & 0.329622 & 0.659245 & 0.670378 \tabularnewline
45 & 0.300463 & 0.600927 & 0.699537 \tabularnewline
46 & 0.295864 & 0.591728 & 0.704136 \tabularnewline
47 & 0.279558 & 0.559117 & 0.720442 \tabularnewline
48 & 0.273683 & 0.547366 & 0.726317 \tabularnewline
49 & 0.230331 & 0.460662 & 0.769669 \tabularnewline
50 & 0.227634 & 0.455269 & 0.772366 \tabularnewline
51 & 0.231951 & 0.463903 & 0.768049 \tabularnewline
52 & 0.224005 & 0.44801 & 0.775995 \tabularnewline
53 & 0.22651 & 0.45302 & 0.77349 \tabularnewline
54 & 0.198187 & 0.396374 & 0.801813 \tabularnewline
55 & 0.176791 & 0.353581 & 0.823209 \tabularnewline
56 & 0.340086 & 0.680172 & 0.659914 \tabularnewline
57 & 0.383416 & 0.766833 & 0.616584 \tabularnewline
58 & 0.355923 & 0.711846 & 0.644077 \tabularnewline
59 & 0.352608 & 0.705216 & 0.647392 \tabularnewline
60 & 0.305071 & 0.610143 & 0.694929 \tabularnewline
61 & 0.30722 & 0.61444 & 0.69278 \tabularnewline
62 & 0.332989 & 0.665978 & 0.667011 \tabularnewline
63 & 0.350036 & 0.700072 & 0.649964 \tabularnewline
64 & 0.296219 & 0.592439 & 0.703781 \tabularnewline
65 & 0.298333 & 0.596666 & 0.701667 \tabularnewline
66 & 0.266619 & 0.533238 & 0.733381 \tabularnewline
67 & 0.342222 & 0.684443 & 0.657778 \tabularnewline
68 & 0.634129 & 0.731742 & 0.365871 \tabularnewline
69 & 0.672505 & 0.65499 & 0.327495 \tabularnewline
70 & 0.613506 & 0.772988 & 0.386494 \tabularnewline
71 & 0.595711 & 0.808577 & 0.404289 \tabularnewline
72 & 0.5341 & 0.931801 & 0.4659 \tabularnewline
73 & 0.541615 & 0.91677 & 0.458385 \tabularnewline
74 & 0.567902 & 0.864197 & 0.432098 \tabularnewline
75 & 0.556181 & 0.887638 & 0.443819 \tabularnewline
76 & 0.492596 & 0.985192 & 0.507404 \tabularnewline
77 & 0.514426 & 0.971147 & 0.485574 \tabularnewline
78 & 0.474634 & 0.949268 & 0.525366 \tabularnewline
79 & 0.405577 & 0.811155 & 0.594423 \tabularnewline
80 & 0.333617 & 0.667234 & 0.666383 \tabularnewline
81 & 0.313477 & 0.626954 & 0.686523 \tabularnewline
82 & 0.294038 & 0.588077 & 0.705962 \tabularnewline
83 & 0.236368 & 0.472736 & 0.763632 \tabularnewline
84 & 0.182823 & 0.365646 & 0.817177 \tabularnewline
85 & 0.200109 & 0.400219 & 0.799891 \tabularnewline
86 & 0.165925 & 0.331851 & 0.834075 \tabularnewline
87 & 0.507932 & 0.984136 & 0.492068 \tabularnewline
88 & 0.417164 & 0.834329 & 0.582836 \tabularnewline
89 & 0.357345 & 0.714689 & 0.642655 \tabularnewline
90 & 0.385759 & 0.771517 & 0.614241 \tabularnewline
91 & 0.32468 & 0.649359 & 0.67532 \tabularnewline
92 & 0.23223 & 0.464461 & 0.76777 \tabularnewline
93 & 0.571302 & 0.857396 & 0.428698 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268774&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]0.0636011[/C][C]0.127202[/C][C]0.936399[/C][/ROW]
[ROW][C]11[/C][C]0.261222[/C][C]0.522443[/C][C]0.738778[/C][/ROW]
[ROW][C]12[/C][C]0.159649[/C][C]0.319297[/C][C]0.840351[/C][/ROW]
[ROW][C]13[/C][C]0.428374[/C][C]0.856747[/C][C]0.571626[/C][/ROW]
[ROW][C]14[/C][C]0.31924[/C][C]0.63848[/C][C]0.68076[/C][/ROW]
[ROW][C]15[/C][C]0.824941[/C][C]0.350117[/C][C]0.175059[/C][/ROW]
[ROW][C]16[/C][C]0.756624[/C][C]0.486753[/C][C]0.243376[/C][/ROW]
[ROW][C]17[/C][C]0.785683[/C][C]0.428634[/C][C]0.214317[/C][/ROW]
[ROW][C]18[/C][C]0.785645[/C][C]0.428711[/C][C]0.214355[/C][/ROW]
[ROW][C]19[/C][C]0.720466[/C][C]0.559068[/C][C]0.279534[/C][/ROW]
[ROW][C]20[/C][C]0.657935[/C][C]0.68413[/C][C]0.342065[/C][/ROW]
[ROW][C]21[/C][C]0.728468[/C][C]0.543064[/C][C]0.271532[/C][/ROW]
[ROW][C]22[/C][C]0.694311[/C][C]0.611377[/C][C]0.305689[/C][/ROW]
[ROW][C]23[/C][C]0.703537[/C][C]0.592926[/C][C]0.296463[/C][/ROW]
[ROW][C]24[/C][C]0.63593[/C][C]0.728141[/C][C]0.36407[/C][/ROW]
[ROW][C]25[/C][C]0.571867[/C][C]0.856265[/C][C]0.428133[/C][/ROW]
[ROW][C]26[/C][C]0.647143[/C][C]0.705715[/C][C]0.352857[/C][/ROW]
[ROW][C]27[/C][C]0.590401[/C][C]0.819198[/C][C]0.409599[/C][/ROW]
[ROW][C]28[/C][C]0.552105[/C][C]0.895789[/C][C]0.447895[/C][/ROW]
[ROW][C]29[/C][C]0.540355[/C][C]0.919291[/C][C]0.459645[/C][/ROW]
[ROW][C]30[/C][C]0.48427[/C][C]0.96854[/C][C]0.51573[/C][/ROW]
[ROW][C]31[/C][C]0.512985[/C][C]0.974029[/C][C]0.487015[/C][/ROW]
[ROW][C]32[/C][C]0.473611[/C][C]0.947223[/C][C]0.526389[/C][/ROW]
[ROW][C]33[/C][C]0.571365[/C][C]0.857269[/C][C]0.428635[/C][/ROW]
[ROW][C]34[/C][C]0.536095[/C][C]0.927809[/C][C]0.463905[/C][/ROW]
[ROW][C]35[/C][C]0.476914[/C][C]0.953829[/C][C]0.523086[/C][/ROW]
[ROW][C]36[/C][C]0.49508[/C][C]0.990159[/C][C]0.50492[/C][/ROW]
[ROW][C]37[/C][C]0.432996[/C][C]0.865991[/C][C]0.567004[/C][/ROW]
[ROW][C]38[/C][C]0.440802[/C][C]0.881603[/C][C]0.559198[/C][/ROW]
[ROW][C]39[/C][C]0.379568[/C][C]0.759137[/C][C]0.620432[/C][/ROW]
[ROW][C]40[/C][C]0.424226[/C][C]0.848452[/C][C]0.575774[/C][/ROW]
[ROW][C]41[/C][C]0.459199[/C][C]0.918397[/C][C]0.540801[/C][/ROW]
[ROW][C]42[/C][C]0.438738[/C][C]0.877477[/C][C]0.561262[/C][/ROW]
[ROW][C]43[/C][C]0.385173[/C][C]0.770345[/C][C]0.614827[/C][/ROW]
[ROW][C]44[/C][C]0.329622[/C][C]0.659245[/C][C]0.670378[/C][/ROW]
[ROW][C]45[/C][C]0.300463[/C][C]0.600927[/C][C]0.699537[/C][/ROW]
[ROW][C]46[/C][C]0.295864[/C][C]0.591728[/C][C]0.704136[/C][/ROW]
[ROW][C]47[/C][C]0.279558[/C][C]0.559117[/C][C]0.720442[/C][/ROW]
[ROW][C]48[/C][C]0.273683[/C][C]0.547366[/C][C]0.726317[/C][/ROW]
[ROW][C]49[/C][C]0.230331[/C][C]0.460662[/C][C]0.769669[/C][/ROW]
[ROW][C]50[/C][C]0.227634[/C][C]0.455269[/C][C]0.772366[/C][/ROW]
[ROW][C]51[/C][C]0.231951[/C][C]0.463903[/C][C]0.768049[/C][/ROW]
[ROW][C]52[/C][C]0.224005[/C][C]0.44801[/C][C]0.775995[/C][/ROW]
[ROW][C]53[/C][C]0.22651[/C][C]0.45302[/C][C]0.77349[/C][/ROW]
[ROW][C]54[/C][C]0.198187[/C][C]0.396374[/C][C]0.801813[/C][/ROW]
[ROW][C]55[/C][C]0.176791[/C][C]0.353581[/C][C]0.823209[/C][/ROW]
[ROW][C]56[/C][C]0.340086[/C][C]0.680172[/C][C]0.659914[/C][/ROW]
[ROW][C]57[/C][C]0.383416[/C][C]0.766833[/C][C]0.616584[/C][/ROW]
[ROW][C]58[/C][C]0.355923[/C][C]0.711846[/C][C]0.644077[/C][/ROW]
[ROW][C]59[/C][C]0.352608[/C][C]0.705216[/C][C]0.647392[/C][/ROW]
[ROW][C]60[/C][C]0.305071[/C][C]0.610143[/C][C]0.694929[/C][/ROW]
[ROW][C]61[/C][C]0.30722[/C][C]0.61444[/C][C]0.69278[/C][/ROW]
[ROW][C]62[/C][C]0.332989[/C][C]0.665978[/C][C]0.667011[/C][/ROW]
[ROW][C]63[/C][C]0.350036[/C][C]0.700072[/C][C]0.649964[/C][/ROW]
[ROW][C]64[/C][C]0.296219[/C][C]0.592439[/C][C]0.703781[/C][/ROW]
[ROW][C]65[/C][C]0.298333[/C][C]0.596666[/C][C]0.701667[/C][/ROW]
[ROW][C]66[/C][C]0.266619[/C][C]0.533238[/C][C]0.733381[/C][/ROW]
[ROW][C]67[/C][C]0.342222[/C][C]0.684443[/C][C]0.657778[/C][/ROW]
[ROW][C]68[/C][C]0.634129[/C][C]0.731742[/C][C]0.365871[/C][/ROW]
[ROW][C]69[/C][C]0.672505[/C][C]0.65499[/C][C]0.327495[/C][/ROW]
[ROW][C]70[/C][C]0.613506[/C][C]0.772988[/C][C]0.386494[/C][/ROW]
[ROW][C]71[/C][C]0.595711[/C][C]0.808577[/C][C]0.404289[/C][/ROW]
[ROW][C]72[/C][C]0.5341[/C][C]0.931801[/C][C]0.4659[/C][/ROW]
[ROW][C]73[/C][C]0.541615[/C][C]0.91677[/C][C]0.458385[/C][/ROW]
[ROW][C]74[/C][C]0.567902[/C][C]0.864197[/C][C]0.432098[/C][/ROW]
[ROW][C]75[/C][C]0.556181[/C][C]0.887638[/C][C]0.443819[/C][/ROW]
[ROW][C]76[/C][C]0.492596[/C][C]0.985192[/C][C]0.507404[/C][/ROW]
[ROW][C]77[/C][C]0.514426[/C][C]0.971147[/C][C]0.485574[/C][/ROW]
[ROW][C]78[/C][C]0.474634[/C][C]0.949268[/C][C]0.525366[/C][/ROW]
[ROW][C]79[/C][C]0.405577[/C][C]0.811155[/C][C]0.594423[/C][/ROW]
[ROW][C]80[/C][C]0.333617[/C][C]0.667234[/C][C]0.666383[/C][/ROW]
[ROW][C]81[/C][C]0.313477[/C][C]0.626954[/C][C]0.686523[/C][/ROW]
[ROW][C]82[/C][C]0.294038[/C][C]0.588077[/C][C]0.705962[/C][/ROW]
[ROW][C]83[/C][C]0.236368[/C][C]0.472736[/C][C]0.763632[/C][/ROW]
[ROW][C]84[/C][C]0.182823[/C][C]0.365646[/C][C]0.817177[/C][/ROW]
[ROW][C]85[/C][C]0.200109[/C][C]0.400219[/C][C]0.799891[/C][/ROW]
[ROW][C]86[/C][C]0.165925[/C][C]0.331851[/C][C]0.834075[/C][/ROW]
[ROW][C]87[/C][C]0.507932[/C][C]0.984136[/C][C]0.492068[/C][/ROW]
[ROW][C]88[/C][C]0.417164[/C][C]0.834329[/C][C]0.582836[/C][/ROW]
[ROW][C]89[/C][C]0.357345[/C][C]0.714689[/C][C]0.642655[/C][/ROW]
[ROW][C]90[/C][C]0.385759[/C][C]0.771517[/C][C]0.614241[/C][/ROW]
[ROW][C]91[/C][C]0.32468[/C][C]0.649359[/C][C]0.67532[/C][/ROW]
[ROW][C]92[/C][C]0.23223[/C][C]0.464461[/C][C]0.76777[/C][/ROW]
[ROW][C]93[/C][C]0.571302[/C][C]0.857396[/C][C]0.428698[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268774&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268774&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
100.06360110.1272020.936399
110.2612220.5224430.738778
120.1596490.3192970.840351
130.4283740.8567470.571626
140.319240.638480.68076
150.8249410.3501170.175059
160.7566240.4867530.243376
170.7856830.4286340.214317
180.7856450.4287110.214355
190.7204660.5590680.279534
200.6579350.684130.342065
210.7284680.5430640.271532
220.6943110.6113770.305689
230.7035370.5929260.296463
240.635930.7281410.36407
250.5718670.8562650.428133
260.6471430.7057150.352857
270.5904010.8191980.409599
280.5521050.8957890.447895
290.5403550.9192910.459645
300.484270.968540.51573
310.5129850.9740290.487015
320.4736110.9472230.526389
330.5713650.8572690.428635
340.5360950.9278090.463905
350.4769140.9538290.523086
360.495080.9901590.50492
370.4329960.8659910.567004
380.4408020.8816030.559198
390.3795680.7591370.620432
400.4242260.8484520.575774
410.4591990.9183970.540801
420.4387380.8774770.561262
430.3851730.7703450.614827
440.3296220.6592450.670378
450.3004630.6009270.699537
460.2958640.5917280.704136
470.2795580.5591170.720442
480.2736830.5473660.726317
490.2303310.4606620.769669
500.2276340.4552690.772366
510.2319510.4639030.768049
520.2240050.448010.775995
530.226510.453020.77349
540.1981870.3963740.801813
550.1767910.3535810.823209
560.3400860.6801720.659914
570.3834160.7668330.616584
580.3559230.7118460.644077
590.3526080.7052160.647392
600.3050710.6101430.694929
610.307220.614440.69278
620.3329890.6659780.667011
630.3500360.7000720.649964
640.2962190.5924390.703781
650.2983330.5966660.701667
660.2666190.5332380.733381
670.3422220.6844430.657778
680.6341290.7317420.365871
690.6725050.654990.327495
700.6135060.7729880.386494
710.5957110.8085770.404289
720.53410.9318010.4659
730.5416150.916770.458385
740.5679020.8641970.432098
750.5561810.8876380.443819
760.4925960.9851920.507404
770.5144260.9711470.485574
780.4746340.9492680.525366
790.4055770.8111550.594423
800.3336170.6672340.666383
810.3134770.6269540.686523
820.2940380.5880770.705962
830.2363680.4727360.763632
840.1828230.3656460.817177
850.2001090.4002190.799891
860.1659250.3318510.834075
870.5079320.9841360.492068
880.4171640.8343290.582836
890.3573450.7146890.642655
900.3857590.7715170.614241
910.324680.6493590.67532
920.232230.4644610.76777
930.5713020.8573960.428698






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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268774&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268774&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268774&T=6

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


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

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


Figures (Output of Computation)

Figure 1
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Figure 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 10
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Input Parameters & R Code

Parameters (Session):
par1 = 7 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 7 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}