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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 14:19:03 +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/t14186536241zbi2ynvvuv57zk.htm/, Retrieved Thu, 31 Oct 2024 23:37:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=268491, Retrieved Thu, 31 Oct 2024 23:37:41 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact112
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-12-15 14:19:03] [d6d52749fb51c32aec4577a0cf80c32e] [Current]
-         [Multiple Regression] [] [2014-12-15 14:48:33] [140911b40e1d4634498cdfdad7246f4c]
- RM D    [Paired and Unpaired Two Samples Tests about the Mean] [] [2014-12-15 15:25:49] [140911b40e1d4634498cdfdad7246f4c]
- RM D    [Paired and Unpaired Two Samples Tests about the Mean] [] [2014-12-15 15:40:50] [140911b40e1d4634498cdfdad7246f4c]
- RM D    [Paired and Unpaired Two Samples Tests about the Mean] [] [2014-12-15 15:55:41] [140911b40e1d4634498cdfdad7246f4c]
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Dataseries X:
1.8 12.9
2.1 12.2
2.2 12.8
2.3 7.4
2.1 6.7
2.7 12.6
2.1 14.8
2.4 13.3
2.9 11.1
2.2 8.2
2.1 11.4
2.2 6.4
2.2 10.6
2.7 12.0
1.9 6.3
2.0 11.3
2.5 11.9
2.2 9.3
2.3 9.6
1.9 10.0
2.1 6.4
3.5 13.8
2.1 10.8
2.3 13.8
2.3 11.7
2.2 10.9
3.5 16.1
1.9 13.4
1.9 9.9
1.9 11.5
1.9 8.3
2.1 11.7
2.0 9.0
3.2 9.7
2.3 10.8
2.5 10.3
1.8 10.4
2.4 12.7
2.8 9.3
2.3 11.8
2.0 5.9
2.5 11.4
2.3 13.0
1.8 10.8
1.9 12.3
2.6 11.3
2.0 11.8
2.6 7.9
1.6 12.7
2.2 12.3
2.1 11.6
1.8 6.7
1.8 10.9
1.9 12.1
2.4 13.3
1.9 10.1
2.0 5.7
2.1 14.3
1.7 8.0
1.9 13.3
2.1 9.3
2.4 12.5
1.8 7.6
2.3 15.9
2.1 9.2
2.0 9.1
2.8 11.1
2.0 13.0
2.7 14.5
2.1 12.2
2.9 12.3
2.0 11.4
1.8 8.8
2.6 14.6
2.1 12.6
2.3 NA
2.3 13.0
2.2 12.6
2.0 13.2
2.2 9.9
2.1 7.7
2.1 10.5
1.9 13.4
2.0 10.9
1.7 4.3
2.2 10.3
2.2 11.8
2.3 11.2
2.4 11.4
2.1 8.6
1.9 13.2
1.7 12.6
1.8 5.6
1.5 9.9
1.9 8.8
1.9 7.7
1.7 9.0
1.9 7.3
1.9 11.4
1.8 13.6
2.4 7.9
1.8 10.7
1.9 10.3
1.8 8.3
2.1 9.6
1.9 14.2
2.2 8.5
2.0 13.5
1.7 4.9
1.7 6.4
1.8 9.6
1.9 11.6




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 5 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268491&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268491&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268491&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 5.79858 + 2.27707PR[t] + 0.000248562t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  5.79858 +  2.27707PR[t] +  0.000248562t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268491&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  5.79858 +  2.27707PR[t] +  0.000248562t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268491&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268491&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 5.79858 + 2.27707PR[t] + 0.000248562t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.798581.649233.5160.0006418540.000320927
PR2.277070.6731053.3830.0009999040.000499952
t0.0002485620.007400690.033590.9732690.486634

\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) & 5.79858 & 1.64923 & 3.516 & 0.000641854 & 0.000320927 \tabularnewline
PR & 2.27707 & 0.673105 & 3.383 & 0.000999904 & 0.000499952 \tabularnewline
t & 0.000248562 & 0.00740069 & 0.03359 & 0.973269 & 0.486634 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268491&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]5.79858[/C][C]1.64923[/C][C]3.516[/C][C]0.000641854[/C][C]0.000320927[/C][/ROW]
[ROW][C]PR[/C][C]2.27707[/C][C]0.673105[/C][C]3.383[/C][C]0.000999904[/C][C]0.000499952[/C][/ROW]
[ROW][C]t[/C][C]0.000248562[/C][C]0.00740069[/C][C]0.03359[/C][C]0.973269[/C][C]0.486634[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268491&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268491&T=2

As an alternative you can also use a 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)5.798581.649233.5160.0006418540.000320927
PR2.277070.6731053.3830.0009999040.000499952
t0.0002485620.007400690.033590.9732690.486634







Multiple Linear Regression - Regression Statistics
Multiple R0.327367
R-squared0.107169
Adjusted R-squared0.090635
F-TEST (value)6.48176
F-TEST (DF numerator)2
F-TEST (DF denominator)108
p-value0.00219555
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.36708
Sum Squared Residuals605.129

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.327367 \tabularnewline
R-squared & 0.107169 \tabularnewline
Adjusted R-squared & 0.090635 \tabularnewline
F-TEST (value) & 6.48176 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 108 \tabularnewline
p-value & 0.00219555 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.36708 \tabularnewline
Sum Squared Residuals & 605.129 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268491&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.327367[/C][/ROW]
[ROW][C]R-squared[/C][C]0.107169[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.090635[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]6.48176[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]108[/C][/ROW]
[ROW][C]p-value[/C][C]0.00219555[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.36708[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]605.129[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268491&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268491&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.327367
R-squared0.107169
Adjusted R-squared0.090635
F-TEST (value)6.48176
F-TEST (DF numerator)2
F-TEST (DF denominator)108
p-value0.00219555
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.36708
Sum Squared Residuals605.129







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.99.897553.00245
212.210.58091.61908
312.810.80891.99113
47.411.0368-3.63683
56.710.5817-3.88167
612.611.94820.651847
714.810.58224.21784
813.311.26552.03447
911.112.4043-1.30431
108.210.8106-2.61061
1111.410.58320.816843
126.410.8111-4.41111
1310.610.8114-0.21136
141211.95010.0498581
156.310.1287-3.82874
1611.310.35670.943307
1711.911.49550.404526
189.310.8126-1.5126
199.611.0406-1.44056
201010.13-0.129981
216.410.5856-4.18564
2213.813.77380.026217
2310.810.58610.213861
2413.811.04182.7582
2511.711.0420.65795
2610.910.81460.0854083
2716.113.7752.32497
2813.410.1323.26803
299.910.1322-0.232218
3011.510.13251.36753
318.310.1327-1.83271
3211.710.58841.11162
33910.3609-1.36092
349.713.0936-3.39365
3510.811.0445-0.244535
3610.311.5002-1.2002
3710.49.90650.4935
3812.711.2731.42701
399.312.1841-2.88406
4011.811.04580.754222
415.910.3629-4.46291
4211.411.5017-0.101688
431311.04651.95348
4410.89.908240.89176
4512.310.13622.16381
4611.311.7304-0.430389
4711.810.36441.4356
487.911.7309-3.83089
4912.79.454073.24593
5012.310.82061.47944
5111.610.59311.0069
526.79.91023-3.21023
5310.99.910480.989523
5412.110.13841.96157
5513.311.27722.02279
5610.110.1389-0.0389288
575.710.3669-4.66688
5814.310.59483.70516
5989.68426-1.68426
6013.310.13993.16008
619.310.5956-1.29558
6212.511.2791.22105
637.69.91296-2.31296
6415.911.05174.84826
659.210.5966-1.39658
669.110.3691-1.26912
6711.112.191-1.09102
681310.36962.63038
6914.511.96382.53619
7012.210.59781.60218
7112.312.4197-0.119723
7211.410.37061.02939
738.89.91545-1.11545
7414.611.73732.86265
7512.610.59912.00094
76NANA1.94503
771311.22751.77248
7812.69.772352.82765
7913.214.128-0.928014
809.912.8006-2.90056
817.77.8008-0.100805
8210.57.245643.25436
8313.412.87360.526405
8410.916.2907-5.39072
854.34.82951-0.529505
8610.39.329750.970246
8711.811.65770.142291
8811.211.08570.114336
8911.413.4028-2.00279
908.65.547633.05237
9113.210.29252.90754
9212.616.9204-4.32042
935.64.937550.662452
949.911.2486-1.34862
958.811.2489-2.44887
967.78.39371-0.693707
97911.8494-2.84937
987.36.049621.25038
9911.47.722163.67784
10013.616.9886-3.38865
1017.97.122660.777344
10210.710.55060.149389
10310.311.9232-1.62315
1048.39.30652-1.00652
1059.65.551364.04864
10614.216.5347-2.33473
1078.55.379563.12044
10813.518.2967-4.79669
1094.98.19694-3.29694
1106.46.72489-0.324893
1119.68.152851.44715
11211.6NANA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 9.89755 & 3.00245 \tabularnewline
2 & 12.2 & 10.5809 & 1.61908 \tabularnewline
3 & 12.8 & 10.8089 & 1.99113 \tabularnewline
4 & 7.4 & 11.0368 & -3.63683 \tabularnewline
5 & 6.7 & 10.5817 & -3.88167 \tabularnewline
6 & 12.6 & 11.9482 & 0.651847 \tabularnewline
7 & 14.8 & 10.5822 & 4.21784 \tabularnewline
8 & 13.3 & 11.2655 & 2.03447 \tabularnewline
9 & 11.1 & 12.4043 & -1.30431 \tabularnewline
10 & 8.2 & 10.8106 & -2.61061 \tabularnewline
11 & 11.4 & 10.5832 & 0.816843 \tabularnewline
12 & 6.4 & 10.8111 & -4.41111 \tabularnewline
13 & 10.6 & 10.8114 & -0.21136 \tabularnewline
14 & 12 & 11.9501 & 0.0498581 \tabularnewline
15 & 6.3 & 10.1287 & -3.82874 \tabularnewline
16 & 11.3 & 10.3567 & 0.943307 \tabularnewline
17 & 11.9 & 11.4955 & 0.404526 \tabularnewline
18 & 9.3 & 10.8126 & -1.5126 \tabularnewline
19 & 9.6 & 11.0406 & -1.44056 \tabularnewline
20 & 10 & 10.13 & -0.129981 \tabularnewline
21 & 6.4 & 10.5856 & -4.18564 \tabularnewline
22 & 13.8 & 13.7738 & 0.026217 \tabularnewline
23 & 10.8 & 10.5861 & 0.213861 \tabularnewline
24 & 13.8 & 11.0418 & 2.7582 \tabularnewline
25 & 11.7 & 11.042 & 0.65795 \tabularnewline
26 & 10.9 & 10.8146 & 0.0854083 \tabularnewline
27 & 16.1 & 13.775 & 2.32497 \tabularnewline
28 & 13.4 & 10.132 & 3.26803 \tabularnewline
29 & 9.9 & 10.1322 & -0.232218 \tabularnewline
30 & 11.5 & 10.1325 & 1.36753 \tabularnewline
31 & 8.3 & 10.1327 & -1.83271 \tabularnewline
32 & 11.7 & 10.5884 & 1.11162 \tabularnewline
33 & 9 & 10.3609 & -1.36092 \tabularnewline
34 & 9.7 & 13.0936 & -3.39365 \tabularnewline
35 & 10.8 & 11.0445 & -0.244535 \tabularnewline
36 & 10.3 & 11.5002 & -1.2002 \tabularnewline
37 & 10.4 & 9.9065 & 0.4935 \tabularnewline
38 & 12.7 & 11.273 & 1.42701 \tabularnewline
39 & 9.3 & 12.1841 & -2.88406 \tabularnewline
40 & 11.8 & 11.0458 & 0.754222 \tabularnewline
41 & 5.9 & 10.3629 & -4.46291 \tabularnewline
42 & 11.4 & 11.5017 & -0.101688 \tabularnewline
43 & 13 & 11.0465 & 1.95348 \tabularnewline
44 & 10.8 & 9.90824 & 0.89176 \tabularnewline
45 & 12.3 & 10.1362 & 2.16381 \tabularnewline
46 & 11.3 & 11.7304 & -0.430389 \tabularnewline
47 & 11.8 & 10.3644 & 1.4356 \tabularnewline
48 & 7.9 & 11.7309 & -3.83089 \tabularnewline
49 & 12.7 & 9.45407 & 3.24593 \tabularnewline
50 & 12.3 & 10.8206 & 1.47944 \tabularnewline
51 & 11.6 & 10.5931 & 1.0069 \tabularnewline
52 & 6.7 & 9.91023 & -3.21023 \tabularnewline
53 & 10.9 & 9.91048 & 0.989523 \tabularnewline
54 & 12.1 & 10.1384 & 1.96157 \tabularnewline
55 & 13.3 & 11.2772 & 2.02279 \tabularnewline
56 & 10.1 & 10.1389 & -0.0389288 \tabularnewline
57 & 5.7 & 10.3669 & -4.66688 \tabularnewline
58 & 14.3 & 10.5948 & 3.70516 \tabularnewline
59 & 8 & 9.68426 & -1.68426 \tabularnewline
60 & 13.3 & 10.1399 & 3.16008 \tabularnewline
61 & 9.3 & 10.5956 & -1.29558 \tabularnewline
62 & 12.5 & 11.279 & 1.22105 \tabularnewline
63 & 7.6 & 9.91296 & -2.31296 \tabularnewline
64 & 15.9 & 11.0517 & 4.84826 \tabularnewline
65 & 9.2 & 10.5966 & -1.39658 \tabularnewline
66 & 9.1 & 10.3691 & -1.26912 \tabularnewline
67 & 11.1 & 12.191 & -1.09102 \tabularnewline
68 & 13 & 10.3696 & 2.63038 \tabularnewline
69 & 14.5 & 11.9638 & 2.53619 \tabularnewline
70 & 12.2 & 10.5978 & 1.60218 \tabularnewline
71 & 12.3 & 12.4197 & -0.119723 \tabularnewline
72 & 11.4 & 10.3706 & 1.02939 \tabularnewline
73 & 8.8 & 9.91545 & -1.11545 \tabularnewline
74 & 14.6 & 11.7373 & 2.86265 \tabularnewline
75 & 12.6 & 10.5991 & 2.00094 \tabularnewline
76 & NA & NA & 1.94503 \tabularnewline
77 & 13 & 11.2275 & 1.77248 \tabularnewline
78 & 12.6 & 9.77235 & 2.82765 \tabularnewline
79 & 13.2 & 14.128 & -0.928014 \tabularnewline
80 & 9.9 & 12.8006 & -2.90056 \tabularnewline
81 & 7.7 & 7.8008 & -0.100805 \tabularnewline
82 & 10.5 & 7.24564 & 3.25436 \tabularnewline
83 & 13.4 & 12.8736 & 0.526405 \tabularnewline
84 & 10.9 & 16.2907 & -5.39072 \tabularnewline
85 & 4.3 & 4.82951 & -0.529505 \tabularnewline
86 & 10.3 & 9.32975 & 0.970246 \tabularnewline
87 & 11.8 & 11.6577 & 0.142291 \tabularnewline
88 & 11.2 & 11.0857 & 0.114336 \tabularnewline
89 & 11.4 & 13.4028 & -2.00279 \tabularnewline
90 & 8.6 & 5.54763 & 3.05237 \tabularnewline
91 & 13.2 & 10.2925 & 2.90754 \tabularnewline
92 & 12.6 & 16.9204 & -4.32042 \tabularnewline
93 & 5.6 & 4.93755 & 0.662452 \tabularnewline
94 & 9.9 & 11.2486 & -1.34862 \tabularnewline
95 & 8.8 & 11.2489 & -2.44887 \tabularnewline
96 & 7.7 & 8.39371 & -0.693707 \tabularnewline
97 & 9 & 11.8494 & -2.84937 \tabularnewline
98 & 7.3 & 6.04962 & 1.25038 \tabularnewline
99 & 11.4 & 7.72216 & 3.67784 \tabularnewline
100 & 13.6 & 16.9886 & -3.38865 \tabularnewline
101 & 7.9 & 7.12266 & 0.777344 \tabularnewline
102 & 10.7 & 10.5506 & 0.149389 \tabularnewline
103 & 10.3 & 11.9232 & -1.62315 \tabularnewline
104 & 8.3 & 9.30652 & -1.00652 \tabularnewline
105 & 9.6 & 5.55136 & 4.04864 \tabularnewline
106 & 14.2 & 16.5347 & -2.33473 \tabularnewline
107 & 8.5 & 5.37956 & 3.12044 \tabularnewline
108 & 13.5 & 18.2967 & -4.79669 \tabularnewline
109 & 4.9 & 8.19694 & -3.29694 \tabularnewline
110 & 6.4 & 6.72489 & -0.324893 \tabularnewline
111 & 9.6 & 8.15285 & 1.44715 \tabularnewline
112 & 11.6 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268491&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]12.9[/C][C]9.89755[/C][C]3.00245[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]10.5809[/C][C]1.61908[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]10.8089[/C][C]1.99113[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]11.0368[/C][C]-3.63683[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]10.5817[/C][C]-3.88167[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]11.9482[/C][C]0.651847[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]10.5822[/C][C]4.21784[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]11.2655[/C][C]2.03447[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]12.4043[/C][C]-1.30431[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]10.8106[/C][C]-2.61061[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]10.5832[/C][C]0.816843[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]10.8111[/C][C]-4.41111[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]10.8114[/C][C]-0.21136[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]11.9501[/C][C]0.0498581[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]10.1287[/C][C]-3.82874[/C][/ROW]
[ROW][C]16[/C][C]11.3[/C][C]10.3567[/C][C]0.943307[/C][/ROW]
[ROW][C]17[/C][C]11.9[/C][C]11.4955[/C][C]0.404526[/C][/ROW]
[ROW][C]18[/C][C]9.3[/C][C]10.8126[/C][C]-1.5126[/C][/ROW]
[ROW][C]19[/C][C]9.6[/C][C]11.0406[/C][C]-1.44056[/C][/ROW]
[ROW][C]20[/C][C]10[/C][C]10.13[/C][C]-0.129981[/C][/ROW]
[ROW][C]21[/C][C]6.4[/C][C]10.5856[/C][C]-4.18564[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]13.7738[/C][C]0.026217[/C][/ROW]
[ROW][C]23[/C][C]10.8[/C][C]10.5861[/C][C]0.213861[/C][/ROW]
[ROW][C]24[/C][C]13.8[/C][C]11.0418[/C][C]2.7582[/C][/ROW]
[ROW][C]25[/C][C]11.7[/C][C]11.042[/C][C]0.65795[/C][/ROW]
[ROW][C]26[/C][C]10.9[/C][C]10.8146[/C][C]0.0854083[/C][/ROW]
[ROW][C]27[/C][C]16.1[/C][C]13.775[/C][C]2.32497[/C][/ROW]
[ROW][C]28[/C][C]13.4[/C][C]10.132[/C][C]3.26803[/C][/ROW]
[ROW][C]29[/C][C]9.9[/C][C]10.1322[/C][C]-0.232218[/C][/ROW]
[ROW][C]30[/C][C]11.5[/C][C]10.1325[/C][C]1.36753[/C][/ROW]
[ROW][C]31[/C][C]8.3[/C][C]10.1327[/C][C]-1.83271[/C][/ROW]
[ROW][C]32[/C][C]11.7[/C][C]10.5884[/C][C]1.11162[/C][/ROW]
[ROW][C]33[/C][C]9[/C][C]10.3609[/C][C]-1.36092[/C][/ROW]
[ROW][C]34[/C][C]9.7[/C][C]13.0936[/C][C]-3.39365[/C][/ROW]
[ROW][C]35[/C][C]10.8[/C][C]11.0445[/C][C]-0.244535[/C][/ROW]
[ROW][C]36[/C][C]10.3[/C][C]11.5002[/C][C]-1.2002[/C][/ROW]
[ROW][C]37[/C][C]10.4[/C][C]9.9065[/C][C]0.4935[/C][/ROW]
[ROW][C]38[/C][C]12.7[/C][C]11.273[/C][C]1.42701[/C][/ROW]
[ROW][C]39[/C][C]9.3[/C][C]12.1841[/C][C]-2.88406[/C][/ROW]
[ROW][C]40[/C][C]11.8[/C][C]11.0458[/C][C]0.754222[/C][/ROW]
[ROW][C]41[/C][C]5.9[/C][C]10.3629[/C][C]-4.46291[/C][/ROW]
[ROW][C]42[/C][C]11.4[/C][C]11.5017[/C][C]-0.101688[/C][/ROW]
[ROW][C]43[/C][C]13[/C][C]11.0465[/C][C]1.95348[/C][/ROW]
[ROW][C]44[/C][C]10.8[/C][C]9.90824[/C][C]0.89176[/C][/ROW]
[ROW][C]45[/C][C]12.3[/C][C]10.1362[/C][C]2.16381[/C][/ROW]
[ROW][C]46[/C][C]11.3[/C][C]11.7304[/C][C]-0.430389[/C][/ROW]
[ROW][C]47[/C][C]11.8[/C][C]10.3644[/C][C]1.4356[/C][/ROW]
[ROW][C]48[/C][C]7.9[/C][C]11.7309[/C][C]-3.83089[/C][/ROW]
[ROW][C]49[/C][C]12.7[/C][C]9.45407[/C][C]3.24593[/C][/ROW]
[ROW][C]50[/C][C]12.3[/C][C]10.8206[/C][C]1.47944[/C][/ROW]
[ROW][C]51[/C][C]11.6[/C][C]10.5931[/C][C]1.0069[/C][/ROW]
[ROW][C]52[/C][C]6.7[/C][C]9.91023[/C][C]-3.21023[/C][/ROW]
[ROW][C]53[/C][C]10.9[/C][C]9.91048[/C][C]0.989523[/C][/ROW]
[ROW][C]54[/C][C]12.1[/C][C]10.1384[/C][C]1.96157[/C][/ROW]
[ROW][C]55[/C][C]13.3[/C][C]11.2772[/C][C]2.02279[/C][/ROW]
[ROW][C]56[/C][C]10.1[/C][C]10.1389[/C][C]-0.0389288[/C][/ROW]
[ROW][C]57[/C][C]5.7[/C][C]10.3669[/C][C]-4.66688[/C][/ROW]
[ROW][C]58[/C][C]14.3[/C][C]10.5948[/C][C]3.70516[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]9.68426[/C][C]-1.68426[/C][/ROW]
[ROW][C]60[/C][C]13.3[/C][C]10.1399[/C][C]3.16008[/C][/ROW]
[ROW][C]61[/C][C]9.3[/C][C]10.5956[/C][C]-1.29558[/C][/ROW]
[ROW][C]62[/C][C]12.5[/C][C]11.279[/C][C]1.22105[/C][/ROW]
[ROW][C]63[/C][C]7.6[/C][C]9.91296[/C][C]-2.31296[/C][/ROW]
[ROW][C]64[/C][C]15.9[/C][C]11.0517[/C][C]4.84826[/C][/ROW]
[ROW][C]65[/C][C]9.2[/C][C]10.5966[/C][C]-1.39658[/C][/ROW]
[ROW][C]66[/C][C]9.1[/C][C]10.3691[/C][C]-1.26912[/C][/ROW]
[ROW][C]67[/C][C]11.1[/C][C]12.191[/C][C]-1.09102[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]10.3696[/C][C]2.63038[/C][/ROW]
[ROW][C]69[/C][C]14.5[/C][C]11.9638[/C][C]2.53619[/C][/ROW]
[ROW][C]70[/C][C]12.2[/C][C]10.5978[/C][C]1.60218[/C][/ROW]
[ROW][C]71[/C][C]12.3[/C][C]12.4197[/C][C]-0.119723[/C][/ROW]
[ROW][C]72[/C][C]11.4[/C][C]10.3706[/C][C]1.02939[/C][/ROW]
[ROW][C]73[/C][C]8.8[/C][C]9.91545[/C][C]-1.11545[/C][/ROW]
[ROW][C]74[/C][C]14.6[/C][C]11.7373[/C][C]2.86265[/C][/ROW]
[ROW][C]75[/C][C]12.6[/C][C]10.5991[/C][C]2.00094[/C][/ROW]
[ROW][C]76[/C][C]NA[/C][C]NA[/C][C]1.94503[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]11.2275[/C][C]1.77248[/C][/ROW]
[ROW][C]78[/C][C]12.6[/C][C]9.77235[/C][C]2.82765[/C][/ROW]
[ROW][C]79[/C][C]13.2[/C][C]14.128[/C][C]-0.928014[/C][/ROW]
[ROW][C]80[/C][C]9.9[/C][C]12.8006[/C][C]-2.90056[/C][/ROW]
[ROW][C]81[/C][C]7.7[/C][C]7.8008[/C][C]-0.100805[/C][/ROW]
[ROW][C]82[/C][C]10.5[/C][C]7.24564[/C][C]3.25436[/C][/ROW]
[ROW][C]83[/C][C]13.4[/C][C]12.8736[/C][C]0.526405[/C][/ROW]
[ROW][C]84[/C][C]10.9[/C][C]16.2907[/C][C]-5.39072[/C][/ROW]
[ROW][C]85[/C][C]4.3[/C][C]4.82951[/C][C]-0.529505[/C][/ROW]
[ROW][C]86[/C][C]10.3[/C][C]9.32975[/C][C]0.970246[/C][/ROW]
[ROW][C]87[/C][C]11.8[/C][C]11.6577[/C][C]0.142291[/C][/ROW]
[ROW][C]88[/C][C]11.2[/C][C]11.0857[/C][C]0.114336[/C][/ROW]
[ROW][C]89[/C][C]11.4[/C][C]13.4028[/C][C]-2.00279[/C][/ROW]
[ROW][C]90[/C][C]8.6[/C][C]5.54763[/C][C]3.05237[/C][/ROW]
[ROW][C]91[/C][C]13.2[/C][C]10.2925[/C][C]2.90754[/C][/ROW]
[ROW][C]92[/C][C]12.6[/C][C]16.9204[/C][C]-4.32042[/C][/ROW]
[ROW][C]93[/C][C]5.6[/C][C]4.93755[/C][C]0.662452[/C][/ROW]
[ROW][C]94[/C][C]9.9[/C][C]11.2486[/C][C]-1.34862[/C][/ROW]
[ROW][C]95[/C][C]8.8[/C][C]11.2489[/C][C]-2.44887[/C][/ROW]
[ROW][C]96[/C][C]7.7[/C][C]8.39371[/C][C]-0.693707[/C][/ROW]
[ROW][C]97[/C][C]9[/C][C]11.8494[/C][C]-2.84937[/C][/ROW]
[ROW][C]98[/C][C]7.3[/C][C]6.04962[/C][C]1.25038[/C][/ROW]
[ROW][C]99[/C][C]11.4[/C][C]7.72216[/C][C]3.67784[/C][/ROW]
[ROW][C]100[/C][C]13.6[/C][C]16.9886[/C][C]-3.38865[/C][/ROW]
[ROW][C]101[/C][C]7.9[/C][C]7.12266[/C][C]0.777344[/C][/ROW]
[ROW][C]102[/C][C]10.7[/C][C]10.5506[/C][C]0.149389[/C][/ROW]
[ROW][C]103[/C][C]10.3[/C][C]11.9232[/C][C]-1.62315[/C][/ROW]
[ROW][C]104[/C][C]8.3[/C][C]9.30652[/C][C]-1.00652[/C][/ROW]
[ROW][C]105[/C][C]9.6[/C][C]5.55136[/C][C]4.04864[/C][/ROW]
[ROW][C]106[/C][C]14.2[/C][C]16.5347[/C][C]-2.33473[/C][/ROW]
[ROW][C]107[/C][C]8.5[/C][C]5.37956[/C][C]3.12044[/C][/ROW]
[ROW][C]108[/C][C]13.5[/C][C]18.2967[/C][C]-4.79669[/C][/ROW]
[ROW][C]109[/C][C]4.9[/C][C]8.19694[/C][C]-3.29694[/C][/ROW]
[ROW][C]110[/C][C]6.4[/C][C]6.72489[/C][C]-0.324893[/C][/ROW]
[ROW][C]111[/C][C]9.6[/C][C]8.15285[/C][C]1.44715[/C][/ROW]
[ROW][C]112[/C][C]11.6[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268491&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268491&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.99.897553.00245
212.210.58091.61908
312.810.80891.99113
47.411.0368-3.63683
56.710.5817-3.88167
612.611.94820.651847
714.810.58224.21784
813.311.26552.03447
911.112.4043-1.30431
108.210.8106-2.61061
1111.410.58320.816843
126.410.8111-4.41111
1310.610.8114-0.21136
141211.95010.0498581
156.310.1287-3.82874
1611.310.35670.943307
1711.911.49550.404526
189.310.8126-1.5126
199.611.0406-1.44056
201010.13-0.129981
216.410.5856-4.18564
2213.813.77380.026217
2310.810.58610.213861
2413.811.04182.7582
2511.711.0420.65795
2610.910.81460.0854083
2716.113.7752.32497
2813.410.1323.26803
299.910.1322-0.232218
3011.510.13251.36753
318.310.1327-1.83271
3211.710.58841.11162
33910.3609-1.36092
349.713.0936-3.39365
3510.811.0445-0.244535
3610.311.5002-1.2002
3710.49.90650.4935
3812.711.2731.42701
399.312.1841-2.88406
4011.811.04580.754222
415.910.3629-4.46291
4211.411.5017-0.101688
431311.04651.95348
4410.89.908240.89176
4512.310.13622.16381
4611.311.7304-0.430389
4711.810.36441.4356
487.911.7309-3.83089
4912.79.454073.24593
5012.310.82061.47944
5111.610.59311.0069
526.79.91023-3.21023
5310.99.910480.989523
5412.110.13841.96157
5513.311.27722.02279
5610.110.1389-0.0389288
575.710.3669-4.66688
5814.310.59483.70516
5989.68426-1.68426
6013.310.13993.16008
619.310.5956-1.29558
6212.511.2791.22105
637.69.91296-2.31296
6415.911.05174.84826
659.210.5966-1.39658
669.110.3691-1.26912
6711.112.191-1.09102
681310.36962.63038
6914.511.96382.53619
7012.210.59781.60218
7112.312.4197-0.119723
7211.410.37061.02939
738.89.91545-1.11545
7414.611.73732.86265
7512.610.59912.00094
76NANA1.94503
771311.22751.77248
7812.69.772352.82765
7913.214.128-0.928014
809.912.8006-2.90056
817.77.8008-0.100805
8210.57.245643.25436
8313.412.87360.526405
8410.916.2907-5.39072
854.34.82951-0.529505
8610.39.329750.970246
8711.811.65770.142291
8811.211.08570.114336
8911.413.4028-2.00279
908.65.547633.05237
9113.210.29252.90754
9212.616.9204-4.32042
935.64.937550.662452
949.911.2486-1.34862
958.811.2489-2.44887
967.78.39371-0.693707
97911.8494-2.84937
987.36.049621.25038
9911.47.722163.67784
10013.616.9886-3.38865
1017.97.122660.777344
10210.710.55060.149389
10310.311.9232-1.62315
1048.39.30652-1.00652
1059.65.551364.04864
10614.216.5347-2.33473
1078.55.379563.12044
10813.518.2967-4.79669
1094.98.19694-3.29694
1106.46.72489-0.324893
1119.68.152851.44715
11211.6NANA







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.6231590.7536820.376841
70.9584050.08319060.0415953
80.9326580.1346850.0673423
90.8875960.2248080.112404
100.9026160.1947680.0973841
110.8581790.2836410.141821
120.8967020.2065960.103298
130.8619460.2761080.138054
140.832910.3341810.16709
150.8270990.3458010.172901
160.831350.33730.16865
170.8047040.3905920.195296
180.7506840.4986310.249316
190.6911490.6177020.308851
200.6382180.7235640.361782
210.6729190.6541620.327081
220.6310290.7379420.368971
230.604270.7914610.39573
240.6940490.6119020.305951
250.6515720.6968570.348428
260.5936240.8127520.406376
270.5831850.8336310.416815
280.6484390.7031220.351561
290.5879560.8240870.412044
300.5408420.9183160.459158
310.5196510.9606970.480349
320.4673120.9346240.532688
330.4292180.8584350.570782
340.494530.989060.50547
350.4354190.8708380.564581
360.3900820.7801650.609918
370.3380890.6761770.661911
380.3061690.6123380.693831
390.3305230.6610450.669477
400.2866640.5733280.713336
410.4204460.8408910.579554
420.3714840.7429690.628516
430.3612560.7225130.638744
440.3176640.6353280.682336
450.3053080.6106160.694692
460.2630970.5261930.736903
470.2305610.4611220.769439
480.3295690.6591390.670431
490.3631470.7262940.636853
500.3246430.6492860.675357
510.279620.5592390.72038
520.340840.681680.65916
530.2954150.590830.704585
540.2709310.5418610.729069
550.2477180.4954350.752282
560.2070760.4141530.792924
570.3753820.7507650.624618
580.4238280.8476560.576172
590.4115280.8230560.588472
600.4283960.8567920.571604
610.404570.809140.59543
620.3575090.7150180.642491
630.3802270.7604530.619773
640.5009480.9981050.499052
650.4845740.9691480.515426
660.4675070.9350130.532493
670.4485630.8971270.551437
680.4298260.8596530.570174
690.4063610.8127210.593639
700.3602060.7204110.639794
710.3179050.6358110.682095
720.2695460.5390920.730454
730.2452780.4905560.754722
740.2362190.4724380.763781
750.2078180.4156360.792182
760.1835160.3670320.816484
770.1604980.3209960.839502
780.1696180.3392370.830382
790.141550.28310.85845
800.1617540.3235090.838246
810.1278390.2556790.872161
820.1542190.3084370.845781
830.1248110.2496220.875189
840.3036790.6073590.696321
850.2521940.5043870.747806
860.2108050.4216110.789195
870.1684630.3369260.831537
880.135910.2718190.86409
890.1167990.2335970.883201
900.1423090.2846190.857691
910.1773590.3547180.822641
920.2478040.4956090.752196
930.1950710.3901420.804929
940.1521460.3042920.847854
950.1406150.2812290.859385
960.1052750.210550.894725
970.1335920.2671840.866408
980.09296090.1859220.907039
990.1204980.2409950.879502
1000.1739590.3479190.826041
1010.1293160.2586330.870684
1020.08387830.1677570.916122
1030.04896250.09792490.951038
1040.03212690.06425390.967873
1050.1882260.3764520.811774
1060.8892060.2215870.110794

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.623159 & 0.753682 & 0.376841 \tabularnewline
7 & 0.958405 & 0.0831906 & 0.0415953 \tabularnewline
8 & 0.932658 & 0.134685 & 0.0673423 \tabularnewline
9 & 0.887596 & 0.224808 & 0.112404 \tabularnewline
10 & 0.902616 & 0.194768 & 0.0973841 \tabularnewline
11 & 0.858179 & 0.283641 & 0.141821 \tabularnewline
12 & 0.896702 & 0.206596 & 0.103298 \tabularnewline
13 & 0.861946 & 0.276108 & 0.138054 \tabularnewline
14 & 0.83291 & 0.334181 & 0.16709 \tabularnewline
15 & 0.827099 & 0.345801 & 0.172901 \tabularnewline
16 & 0.83135 & 0.3373 & 0.16865 \tabularnewline
17 & 0.804704 & 0.390592 & 0.195296 \tabularnewline
18 & 0.750684 & 0.498631 & 0.249316 \tabularnewline
19 & 0.691149 & 0.617702 & 0.308851 \tabularnewline
20 & 0.638218 & 0.723564 & 0.361782 \tabularnewline
21 & 0.672919 & 0.654162 & 0.327081 \tabularnewline
22 & 0.631029 & 0.737942 & 0.368971 \tabularnewline
23 & 0.60427 & 0.791461 & 0.39573 \tabularnewline
24 & 0.694049 & 0.611902 & 0.305951 \tabularnewline
25 & 0.651572 & 0.696857 & 0.348428 \tabularnewline
26 & 0.593624 & 0.812752 & 0.406376 \tabularnewline
27 & 0.583185 & 0.833631 & 0.416815 \tabularnewline
28 & 0.648439 & 0.703122 & 0.351561 \tabularnewline
29 & 0.587956 & 0.824087 & 0.412044 \tabularnewline
30 & 0.540842 & 0.918316 & 0.459158 \tabularnewline
31 & 0.519651 & 0.960697 & 0.480349 \tabularnewline
32 & 0.467312 & 0.934624 & 0.532688 \tabularnewline
33 & 0.429218 & 0.858435 & 0.570782 \tabularnewline
34 & 0.49453 & 0.98906 & 0.50547 \tabularnewline
35 & 0.435419 & 0.870838 & 0.564581 \tabularnewline
36 & 0.390082 & 0.780165 & 0.609918 \tabularnewline
37 & 0.338089 & 0.676177 & 0.661911 \tabularnewline
38 & 0.306169 & 0.612338 & 0.693831 \tabularnewline
39 & 0.330523 & 0.661045 & 0.669477 \tabularnewline
40 & 0.286664 & 0.573328 & 0.713336 \tabularnewline
41 & 0.420446 & 0.840891 & 0.579554 \tabularnewline
42 & 0.371484 & 0.742969 & 0.628516 \tabularnewline
43 & 0.361256 & 0.722513 & 0.638744 \tabularnewline
44 & 0.317664 & 0.635328 & 0.682336 \tabularnewline
45 & 0.305308 & 0.610616 & 0.694692 \tabularnewline
46 & 0.263097 & 0.526193 & 0.736903 \tabularnewline
47 & 0.230561 & 0.461122 & 0.769439 \tabularnewline
48 & 0.329569 & 0.659139 & 0.670431 \tabularnewline
49 & 0.363147 & 0.726294 & 0.636853 \tabularnewline
50 & 0.324643 & 0.649286 & 0.675357 \tabularnewline
51 & 0.27962 & 0.559239 & 0.72038 \tabularnewline
52 & 0.34084 & 0.68168 & 0.65916 \tabularnewline
53 & 0.295415 & 0.59083 & 0.704585 \tabularnewline
54 & 0.270931 & 0.541861 & 0.729069 \tabularnewline
55 & 0.247718 & 0.495435 & 0.752282 \tabularnewline
56 & 0.207076 & 0.414153 & 0.792924 \tabularnewline
57 & 0.375382 & 0.750765 & 0.624618 \tabularnewline
58 & 0.423828 & 0.847656 & 0.576172 \tabularnewline
59 & 0.411528 & 0.823056 & 0.588472 \tabularnewline
60 & 0.428396 & 0.856792 & 0.571604 \tabularnewline
61 & 0.40457 & 0.80914 & 0.59543 \tabularnewline
62 & 0.357509 & 0.715018 & 0.642491 \tabularnewline
63 & 0.380227 & 0.760453 & 0.619773 \tabularnewline
64 & 0.500948 & 0.998105 & 0.499052 \tabularnewline
65 & 0.484574 & 0.969148 & 0.515426 \tabularnewline
66 & 0.467507 & 0.935013 & 0.532493 \tabularnewline
67 & 0.448563 & 0.897127 & 0.551437 \tabularnewline
68 & 0.429826 & 0.859653 & 0.570174 \tabularnewline
69 & 0.406361 & 0.812721 & 0.593639 \tabularnewline
70 & 0.360206 & 0.720411 & 0.639794 \tabularnewline
71 & 0.317905 & 0.635811 & 0.682095 \tabularnewline
72 & 0.269546 & 0.539092 & 0.730454 \tabularnewline
73 & 0.245278 & 0.490556 & 0.754722 \tabularnewline
74 & 0.236219 & 0.472438 & 0.763781 \tabularnewline
75 & 0.207818 & 0.415636 & 0.792182 \tabularnewline
76 & 0.183516 & 0.367032 & 0.816484 \tabularnewline
77 & 0.160498 & 0.320996 & 0.839502 \tabularnewline
78 & 0.169618 & 0.339237 & 0.830382 \tabularnewline
79 & 0.14155 & 0.2831 & 0.85845 \tabularnewline
80 & 0.161754 & 0.323509 & 0.838246 \tabularnewline
81 & 0.127839 & 0.255679 & 0.872161 \tabularnewline
82 & 0.154219 & 0.308437 & 0.845781 \tabularnewline
83 & 0.124811 & 0.249622 & 0.875189 \tabularnewline
84 & 0.303679 & 0.607359 & 0.696321 \tabularnewline
85 & 0.252194 & 0.504387 & 0.747806 \tabularnewline
86 & 0.210805 & 0.421611 & 0.789195 \tabularnewline
87 & 0.168463 & 0.336926 & 0.831537 \tabularnewline
88 & 0.13591 & 0.271819 & 0.86409 \tabularnewline
89 & 0.116799 & 0.233597 & 0.883201 \tabularnewline
90 & 0.142309 & 0.284619 & 0.857691 \tabularnewline
91 & 0.177359 & 0.354718 & 0.822641 \tabularnewline
92 & 0.247804 & 0.495609 & 0.752196 \tabularnewline
93 & 0.195071 & 0.390142 & 0.804929 \tabularnewline
94 & 0.152146 & 0.304292 & 0.847854 \tabularnewline
95 & 0.140615 & 0.281229 & 0.859385 \tabularnewline
96 & 0.105275 & 0.21055 & 0.894725 \tabularnewline
97 & 0.133592 & 0.267184 & 0.866408 \tabularnewline
98 & 0.0929609 & 0.185922 & 0.907039 \tabularnewline
99 & 0.120498 & 0.240995 & 0.879502 \tabularnewline
100 & 0.173959 & 0.347919 & 0.826041 \tabularnewline
101 & 0.129316 & 0.258633 & 0.870684 \tabularnewline
102 & 0.0838783 & 0.167757 & 0.916122 \tabularnewline
103 & 0.0489625 & 0.0979249 & 0.951038 \tabularnewline
104 & 0.0321269 & 0.0642539 & 0.967873 \tabularnewline
105 & 0.188226 & 0.376452 & 0.811774 \tabularnewline
106 & 0.889206 & 0.221587 & 0.110794 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268491&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]6[/C][C]0.623159[/C][C]0.753682[/C][C]0.376841[/C][/ROW]
[ROW][C]7[/C][C]0.958405[/C][C]0.0831906[/C][C]0.0415953[/C][/ROW]
[ROW][C]8[/C][C]0.932658[/C][C]0.134685[/C][C]0.0673423[/C][/ROW]
[ROW][C]9[/C][C]0.887596[/C][C]0.224808[/C][C]0.112404[/C][/ROW]
[ROW][C]10[/C][C]0.902616[/C][C]0.194768[/C][C]0.0973841[/C][/ROW]
[ROW][C]11[/C][C]0.858179[/C][C]0.283641[/C][C]0.141821[/C][/ROW]
[ROW][C]12[/C][C]0.896702[/C][C]0.206596[/C][C]0.103298[/C][/ROW]
[ROW][C]13[/C][C]0.861946[/C][C]0.276108[/C][C]0.138054[/C][/ROW]
[ROW][C]14[/C][C]0.83291[/C][C]0.334181[/C][C]0.16709[/C][/ROW]
[ROW][C]15[/C][C]0.827099[/C][C]0.345801[/C][C]0.172901[/C][/ROW]
[ROW][C]16[/C][C]0.83135[/C][C]0.3373[/C][C]0.16865[/C][/ROW]
[ROW][C]17[/C][C]0.804704[/C][C]0.390592[/C][C]0.195296[/C][/ROW]
[ROW][C]18[/C][C]0.750684[/C][C]0.498631[/C][C]0.249316[/C][/ROW]
[ROW][C]19[/C][C]0.691149[/C][C]0.617702[/C][C]0.308851[/C][/ROW]
[ROW][C]20[/C][C]0.638218[/C][C]0.723564[/C][C]0.361782[/C][/ROW]
[ROW][C]21[/C][C]0.672919[/C][C]0.654162[/C][C]0.327081[/C][/ROW]
[ROW][C]22[/C][C]0.631029[/C][C]0.737942[/C][C]0.368971[/C][/ROW]
[ROW][C]23[/C][C]0.60427[/C][C]0.791461[/C][C]0.39573[/C][/ROW]
[ROW][C]24[/C][C]0.694049[/C][C]0.611902[/C][C]0.305951[/C][/ROW]
[ROW][C]25[/C][C]0.651572[/C][C]0.696857[/C][C]0.348428[/C][/ROW]
[ROW][C]26[/C][C]0.593624[/C][C]0.812752[/C][C]0.406376[/C][/ROW]
[ROW][C]27[/C][C]0.583185[/C][C]0.833631[/C][C]0.416815[/C][/ROW]
[ROW][C]28[/C][C]0.648439[/C][C]0.703122[/C][C]0.351561[/C][/ROW]
[ROW][C]29[/C][C]0.587956[/C][C]0.824087[/C][C]0.412044[/C][/ROW]
[ROW][C]30[/C][C]0.540842[/C][C]0.918316[/C][C]0.459158[/C][/ROW]
[ROW][C]31[/C][C]0.519651[/C][C]0.960697[/C][C]0.480349[/C][/ROW]
[ROW][C]32[/C][C]0.467312[/C][C]0.934624[/C][C]0.532688[/C][/ROW]
[ROW][C]33[/C][C]0.429218[/C][C]0.858435[/C][C]0.570782[/C][/ROW]
[ROW][C]34[/C][C]0.49453[/C][C]0.98906[/C][C]0.50547[/C][/ROW]
[ROW][C]35[/C][C]0.435419[/C][C]0.870838[/C][C]0.564581[/C][/ROW]
[ROW][C]36[/C][C]0.390082[/C][C]0.780165[/C][C]0.609918[/C][/ROW]
[ROW][C]37[/C][C]0.338089[/C][C]0.676177[/C][C]0.661911[/C][/ROW]
[ROW][C]38[/C][C]0.306169[/C][C]0.612338[/C][C]0.693831[/C][/ROW]
[ROW][C]39[/C][C]0.330523[/C][C]0.661045[/C][C]0.669477[/C][/ROW]
[ROW][C]40[/C][C]0.286664[/C][C]0.573328[/C][C]0.713336[/C][/ROW]
[ROW][C]41[/C][C]0.420446[/C][C]0.840891[/C][C]0.579554[/C][/ROW]
[ROW][C]42[/C][C]0.371484[/C][C]0.742969[/C][C]0.628516[/C][/ROW]
[ROW][C]43[/C][C]0.361256[/C][C]0.722513[/C][C]0.638744[/C][/ROW]
[ROW][C]44[/C][C]0.317664[/C][C]0.635328[/C][C]0.682336[/C][/ROW]
[ROW][C]45[/C][C]0.305308[/C][C]0.610616[/C][C]0.694692[/C][/ROW]
[ROW][C]46[/C][C]0.263097[/C][C]0.526193[/C][C]0.736903[/C][/ROW]
[ROW][C]47[/C][C]0.230561[/C][C]0.461122[/C][C]0.769439[/C][/ROW]
[ROW][C]48[/C][C]0.329569[/C][C]0.659139[/C][C]0.670431[/C][/ROW]
[ROW][C]49[/C][C]0.363147[/C][C]0.726294[/C][C]0.636853[/C][/ROW]
[ROW][C]50[/C][C]0.324643[/C][C]0.649286[/C][C]0.675357[/C][/ROW]
[ROW][C]51[/C][C]0.27962[/C][C]0.559239[/C][C]0.72038[/C][/ROW]
[ROW][C]52[/C][C]0.34084[/C][C]0.68168[/C][C]0.65916[/C][/ROW]
[ROW][C]53[/C][C]0.295415[/C][C]0.59083[/C][C]0.704585[/C][/ROW]
[ROW][C]54[/C][C]0.270931[/C][C]0.541861[/C][C]0.729069[/C][/ROW]
[ROW][C]55[/C][C]0.247718[/C][C]0.495435[/C][C]0.752282[/C][/ROW]
[ROW][C]56[/C][C]0.207076[/C][C]0.414153[/C][C]0.792924[/C][/ROW]
[ROW][C]57[/C][C]0.375382[/C][C]0.750765[/C][C]0.624618[/C][/ROW]
[ROW][C]58[/C][C]0.423828[/C][C]0.847656[/C][C]0.576172[/C][/ROW]
[ROW][C]59[/C][C]0.411528[/C][C]0.823056[/C][C]0.588472[/C][/ROW]
[ROW][C]60[/C][C]0.428396[/C][C]0.856792[/C][C]0.571604[/C][/ROW]
[ROW][C]61[/C][C]0.40457[/C][C]0.80914[/C][C]0.59543[/C][/ROW]
[ROW][C]62[/C][C]0.357509[/C][C]0.715018[/C][C]0.642491[/C][/ROW]
[ROW][C]63[/C][C]0.380227[/C][C]0.760453[/C][C]0.619773[/C][/ROW]
[ROW][C]64[/C][C]0.500948[/C][C]0.998105[/C][C]0.499052[/C][/ROW]
[ROW][C]65[/C][C]0.484574[/C][C]0.969148[/C][C]0.515426[/C][/ROW]
[ROW][C]66[/C][C]0.467507[/C][C]0.935013[/C][C]0.532493[/C][/ROW]
[ROW][C]67[/C][C]0.448563[/C][C]0.897127[/C][C]0.551437[/C][/ROW]
[ROW][C]68[/C][C]0.429826[/C][C]0.859653[/C][C]0.570174[/C][/ROW]
[ROW][C]69[/C][C]0.406361[/C][C]0.812721[/C][C]0.593639[/C][/ROW]
[ROW][C]70[/C][C]0.360206[/C][C]0.720411[/C][C]0.639794[/C][/ROW]
[ROW][C]71[/C][C]0.317905[/C][C]0.635811[/C][C]0.682095[/C][/ROW]
[ROW][C]72[/C][C]0.269546[/C][C]0.539092[/C][C]0.730454[/C][/ROW]
[ROW][C]73[/C][C]0.245278[/C][C]0.490556[/C][C]0.754722[/C][/ROW]
[ROW][C]74[/C][C]0.236219[/C][C]0.472438[/C][C]0.763781[/C][/ROW]
[ROW][C]75[/C][C]0.207818[/C][C]0.415636[/C][C]0.792182[/C][/ROW]
[ROW][C]76[/C][C]0.183516[/C][C]0.367032[/C][C]0.816484[/C][/ROW]
[ROW][C]77[/C][C]0.160498[/C][C]0.320996[/C][C]0.839502[/C][/ROW]
[ROW][C]78[/C][C]0.169618[/C][C]0.339237[/C][C]0.830382[/C][/ROW]
[ROW][C]79[/C][C]0.14155[/C][C]0.2831[/C][C]0.85845[/C][/ROW]
[ROW][C]80[/C][C]0.161754[/C][C]0.323509[/C][C]0.838246[/C][/ROW]
[ROW][C]81[/C][C]0.127839[/C][C]0.255679[/C][C]0.872161[/C][/ROW]
[ROW][C]82[/C][C]0.154219[/C][C]0.308437[/C][C]0.845781[/C][/ROW]
[ROW][C]83[/C][C]0.124811[/C][C]0.249622[/C][C]0.875189[/C][/ROW]
[ROW][C]84[/C][C]0.303679[/C][C]0.607359[/C][C]0.696321[/C][/ROW]
[ROW][C]85[/C][C]0.252194[/C][C]0.504387[/C][C]0.747806[/C][/ROW]
[ROW][C]86[/C][C]0.210805[/C][C]0.421611[/C][C]0.789195[/C][/ROW]
[ROW][C]87[/C][C]0.168463[/C][C]0.336926[/C][C]0.831537[/C][/ROW]
[ROW][C]88[/C][C]0.13591[/C][C]0.271819[/C][C]0.86409[/C][/ROW]
[ROW][C]89[/C][C]0.116799[/C][C]0.233597[/C][C]0.883201[/C][/ROW]
[ROW][C]90[/C][C]0.142309[/C][C]0.284619[/C][C]0.857691[/C][/ROW]
[ROW][C]91[/C][C]0.177359[/C][C]0.354718[/C][C]0.822641[/C][/ROW]
[ROW][C]92[/C][C]0.247804[/C][C]0.495609[/C][C]0.752196[/C][/ROW]
[ROW][C]93[/C][C]0.195071[/C][C]0.390142[/C][C]0.804929[/C][/ROW]
[ROW][C]94[/C][C]0.152146[/C][C]0.304292[/C][C]0.847854[/C][/ROW]
[ROW][C]95[/C][C]0.140615[/C][C]0.281229[/C][C]0.859385[/C][/ROW]
[ROW][C]96[/C][C]0.105275[/C][C]0.21055[/C][C]0.894725[/C][/ROW]
[ROW][C]97[/C][C]0.133592[/C][C]0.267184[/C][C]0.866408[/C][/ROW]
[ROW][C]98[/C][C]0.0929609[/C][C]0.185922[/C][C]0.907039[/C][/ROW]
[ROW][C]99[/C][C]0.120498[/C][C]0.240995[/C][C]0.879502[/C][/ROW]
[ROW][C]100[/C][C]0.173959[/C][C]0.347919[/C][C]0.826041[/C][/ROW]
[ROW][C]101[/C][C]0.129316[/C][C]0.258633[/C][C]0.870684[/C][/ROW]
[ROW][C]102[/C][C]0.0838783[/C][C]0.167757[/C][C]0.916122[/C][/ROW]
[ROW][C]103[/C][C]0.0489625[/C][C]0.0979249[/C][C]0.951038[/C][/ROW]
[ROW][C]104[/C][C]0.0321269[/C][C]0.0642539[/C][C]0.967873[/C][/ROW]
[ROW][C]105[/C][C]0.188226[/C][C]0.376452[/C][C]0.811774[/C][/ROW]
[ROW][C]106[/C][C]0.889206[/C][C]0.221587[/C][C]0.110794[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268491&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268491&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.6231590.7536820.376841
70.9584050.08319060.0415953
80.9326580.1346850.0673423
90.8875960.2248080.112404
100.9026160.1947680.0973841
110.8581790.2836410.141821
120.8967020.2065960.103298
130.8619460.2761080.138054
140.832910.3341810.16709
150.8270990.3458010.172901
160.831350.33730.16865
170.8047040.3905920.195296
180.7506840.4986310.249316
190.6911490.6177020.308851
200.6382180.7235640.361782
210.6729190.6541620.327081
220.6310290.7379420.368971
230.604270.7914610.39573
240.6940490.6119020.305951
250.6515720.6968570.348428
260.5936240.8127520.406376
270.5831850.8336310.416815
280.6484390.7031220.351561
290.5879560.8240870.412044
300.5408420.9183160.459158
310.5196510.9606970.480349
320.4673120.9346240.532688
330.4292180.8584350.570782
340.494530.989060.50547
350.4354190.8708380.564581
360.3900820.7801650.609918
370.3380890.6761770.661911
380.3061690.6123380.693831
390.3305230.6610450.669477
400.2866640.5733280.713336
410.4204460.8408910.579554
420.3714840.7429690.628516
430.3612560.7225130.638744
440.3176640.6353280.682336
450.3053080.6106160.694692
460.2630970.5261930.736903
470.2305610.4611220.769439
480.3295690.6591390.670431
490.3631470.7262940.636853
500.3246430.6492860.675357
510.279620.5592390.72038
520.340840.681680.65916
530.2954150.590830.704585
540.2709310.5418610.729069
550.2477180.4954350.752282
560.2070760.4141530.792924
570.3753820.7507650.624618
580.4238280.8476560.576172
590.4115280.8230560.588472
600.4283960.8567920.571604
610.404570.809140.59543
620.3575090.7150180.642491
630.3802270.7604530.619773
640.5009480.9981050.499052
650.4845740.9691480.515426
660.4675070.9350130.532493
670.4485630.8971270.551437
680.4298260.8596530.570174
690.4063610.8127210.593639
700.3602060.7204110.639794
710.3179050.6358110.682095
720.2695460.5390920.730454
730.2452780.4905560.754722
740.2362190.4724380.763781
750.2078180.4156360.792182
760.1835160.3670320.816484
770.1604980.3209960.839502
780.1696180.3392370.830382
790.141550.28310.85845
800.1617540.3235090.838246
810.1278390.2556790.872161
820.1542190.3084370.845781
830.1248110.2496220.875189
840.3036790.6073590.696321
850.2521940.5043870.747806
860.2108050.4216110.789195
870.1684630.3369260.831537
880.135910.2718190.86409
890.1167990.2335970.883201
900.1423090.2846190.857691
910.1773590.3547180.822641
920.2478040.4956090.752196
930.1950710.3901420.804929
940.1521460.3042920.847854
950.1406150.2812290.859385
960.1052750.210550.894725
970.1335920.2671840.866408
980.09296090.1859220.907039
990.1204980.2409950.879502
1000.1739590.3479190.826041
1010.1293160.2586330.870684
1020.08387830.1677570.916122
1030.04896250.09792490.951038
1040.03212690.06425390.967873
1050.1882260.3764520.811774
1060.8892060.2215870.110794







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 level30.029703OK

\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 & 3 & 0.029703 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268491&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]3[/C][C]0.029703[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268491&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268491&T=6

As an alternative you can also use a 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 level30.029703OK



Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}