Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 05 Dec 2008 10:02:07 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/05/t1228496586epl3n2y6j94zztk.htm/, Retrieved Thu, 31 Oct 2024 23:44:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=29342, Retrieved Thu, 31 Oct 2024 23:44:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact230
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Paper - Multiple ...] [2008-12-05 17:02:07] [7957bb37a64ed417bbed8444b0b0ea8a] [Current]
-         [Multiple Regression] [Paper - Multiple ...] [2008-12-08 21:33:52] [fce9014b1ad8484790f3b34d6ba09f7b]
Feedback Forum

Post a new message
Dataseries X:
0	0
9	0
1	0
4	0
6	0
21	0
24	0
23	0
22	0
21	0
20	0
16	0
18	0
18	0
24	0
16	0
15	0
24	0
18	0
15	0
4	0
3	0
6	0
5	0
12	0
12	0
12	0
14	0
12	0
17	0
12	0
20	0
21	0
15	0
22	0
19	0
19	0
26	0
25	0
19	0
20	0
30	0
31	0
35	0
33	0
26	0
25	0
17	0
14	0
8	0
12	0
7	0
4	0
10	0
8	0
16	1
14	1
20	1
9	1
10	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 3 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29342&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29342&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29342&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 time3 seconds
R Server'George Udny Yule' @ 72.249.76.132







Multiple Linear Regression - Estimated Regression Equation
Spa[t] = + 14.32 -4.6Val[t] -1.72M1[t] + 0.280000000000001M2[t] + 0.479999999999999M3[t] -2.32000000000000M4[t] -2.92M5[t] + 6.08M6[t] + 4.28M7[t] + 8.4M8[t] + 5.40000000000001M9[t] + 3.6M10[t] + 3M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Spa[t] =  +  14.32 -4.6Val[t] -1.72M1[t] +  0.280000000000001M2[t] +  0.479999999999999M3[t] -2.32000000000000M4[t] -2.92M5[t] +  6.08M6[t] +  4.28M7[t] +  8.4M8[t] +  5.40000000000001M9[t] +  3.6M10[t] +  3M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29342&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Spa[t] =  +  14.32 -4.6Val[t] -1.72M1[t] +  0.280000000000001M2[t] +  0.479999999999999M3[t] -2.32000000000000M4[t] -2.92M5[t] +  6.08M6[t] +  4.28M7[t] +  8.4M8[t] +  5.40000000000001M9[t] +  3.6M10[t] +  3M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29342&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29342&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
Spa[t] = + 14.32 -4.6Val[t] -1.72M1[t] + 0.280000000000001M2[t] + 0.479999999999999M3[t] -2.32000000000000M4[t] -2.92M5[t] + 6.08M6[t] + 4.28M7[t] + 8.4M8[t] + 5.40000000000001M9[t] + 3.6M10[t] + 3M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)14.323.7193963.85010.0003560.000178
Val-4.64.058194-1.13350.2627490.131375
M1-1.725.197024-0.3310.7421470.371073
M20.2800000000000015.1970240.05390.9572620.478631
M30.4799999999999995.1970240.09240.9268040.463402
M4-2.320000000000005.197024-0.44640.6573520.328676
M5-2.925.197024-0.56190.5768810.288441
M66.085.1970241.16990.247940.12397
M74.285.1970240.82350.4143560.207178
M88.45.1332541.63640.1084380.054219
M95.400000000000015.1332541.0520.2981950.149097
M103.65.1332540.70130.4865680.243284
M1135.1332540.58440.561730.280865

\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) & 14.32 & 3.719396 & 3.8501 & 0.000356 & 0.000178 \tabularnewline
Val & -4.6 & 4.058194 & -1.1335 & 0.262749 & 0.131375 \tabularnewline
M1 & -1.72 & 5.197024 & -0.331 & 0.742147 & 0.371073 \tabularnewline
M2 & 0.280000000000001 & 5.197024 & 0.0539 & 0.957262 & 0.478631 \tabularnewline
M3 & 0.479999999999999 & 5.197024 & 0.0924 & 0.926804 & 0.463402 \tabularnewline
M4 & -2.32000000000000 & 5.197024 & -0.4464 & 0.657352 & 0.328676 \tabularnewline
M5 & -2.92 & 5.197024 & -0.5619 & 0.576881 & 0.288441 \tabularnewline
M6 & 6.08 & 5.197024 & 1.1699 & 0.24794 & 0.12397 \tabularnewline
M7 & 4.28 & 5.197024 & 0.8235 & 0.414356 & 0.207178 \tabularnewline
M8 & 8.4 & 5.133254 & 1.6364 & 0.108438 & 0.054219 \tabularnewline
M9 & 5.40000000000001 & 5.133254 & 1.052 & 0.298195 & 0.149097 \tabularnewline
M10 & 3.6 & 5.133254 & 0.7013 & 0.486568 & 0.243284 \tabularnewline
M11 & 3 & 5.133254 & 0.5844 & 0.56173 & 0.280865 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29342&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]14.32[/C][C]3.719396[/C][C]3.8501[/C][C]0.000356[/C][C]0.000178[/C][/ROW]
[ROW][C]Val[/C][C]-4.6[/C][C]4.058194[/C][C]-1.1335[/C][C]0.262749[/C][C]0.131375[/C][/ROW]
[ROW][C]M1[/C][C]-1.72[/C][C]5.197024[/C][C]-0.331[/C][C]0.742147[/C][C]0.371073[/C][/ROW]
[ROW][C]M2[/C][C]0.280000000000001[/C][C]5.197024[/C][C]0.0539[/C][C]0.957262[/C][C]0.478631[/C][/ROW]
[ROW][C]M3[/C][C]0.479999999999999[/C][C]5.197024[/C][C]0.0924[/C][C]0.926804[/C][C]0.463402[/C][/ROW]
[ROW][C]M4[/C][C]-2.32000000000000[/C][C]5.197024[/C][C]-0.4464[/C][C]0.657352[/C][C]0.328676[/C][/ROW]
[ROW][C]M5[/C][C]-2.92[/C][C]5.197024[/C][C]-0.5619[/C][C]0.576881[/C][C]0.288441[/C][/ROW]
[ROW][C]M6[/C][C]6.08[/C][C]5.197024[/C][C]1.1699[/C][C]0.24794[/C][C]0.12397[/C][/ROW]
[ROW][C]M7[/C][C]4.28[/C][C]5.197024[/C][C]0.8235[/C][C]0.414356[/C][C]0.207178[/C][/ROW]
[ROW][C]M8[/C][C]8.4[/C][C]5.133254[/C][C]1.6364[/C][C]0.108438[/C][C]0.054219[/C][/ROW]
[ROW][C]M9[/C][C]5.40000000000001[/C][C]5.133254[/C][C]1.052[/C][C]0.298195[/C][C]0.149097[/C][/ROW]
[ROW][C]M10[/C][C]3.6[/C][C]5.133254[/C][C]0.7013[/C][C]0.486568[/C][C]0.243284[/C][/ROW]
[ROW][C]M11[/C][C]3[/C][C]5.133254[/C][C]0.5844[/C][C]0.56173[/C][C]0.280865[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29342&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29342&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)14.323.7193963.85010.0003560.000178
Val-4.64.058194-1.13350.2627490.131375
M1-1.725.197024-0.3310.7421470.371073
M20.2800000000000015.1970240.05390.9572620.478631
M30.4799999999999995.1970240.09240.9268040.463402
M4-2.320000000000005.197024-0.44640.6573520.328676
M5-2.925.197024-0.56190.5768810.288441
M66.085.1970241.16990.247940.12397
M74.285.1970240.82350.4143560.207178
M88.45.1332541.63640.1084380.054219
M95.400000000000015.1332541.0520.2981950.149097
M103.65.1332540.70130.4865680.243284
M1135.1332540.58440.561730.280865







Multiple Linear Regression - Regression Statistics
Multiple R0.435053032511856
R-squared0.189271141097762
Adjusted R-squared-0.0177234611751502
F-TEST (value)0.914377181914228
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value0.54019584861609
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.11638741564565
Sum Squared Residuals3096.16

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.435053032511856 \tabularnewline
R-squared & 0.189271141097762 \tabularnewline
Adjusted R-squared & -0.0177234611751502 \tabularnewline
F-TEST (value) & 0.914377181914228 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 0.54019584861609 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 8.11638741564565 \tabularnewline
Sum Squared Residuals & 3096.16 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29342&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.435053032511856[/C][/ROW]
[ROW][C]R-squared[/C][C]0.189271141097762[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0177234611751502[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.914377181914228[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/C][/ROW]
[ROW][C]p-value[/C][C]0.54019584861609[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]8.11638741564565[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]3096.16[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29342&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29342&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 R0.435053032511856
R-squared0.189271141097762
Adjusted R-squared-0.0177234611751502
F-TEST (value)0.914377181914228
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value0.54019584861609
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.11638741564565
Sum Squared Residuals3096.16







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1012.6-12.6
2914.6-5.6
3114.8-13.8
4412-8.00000000000001
5611.4-5.4
62120.40.600000000000003
72418.65.39999999999999
82322.720.280000000000007
92219.722.28
102117.923.07999999999999
112017.322.67999999999999
121614.321.68000000000000
131812.65.4
141814.63.4
152414.89.2
1616124
171511.43.6
182420.43.6
191818.6-0.599999999999998
201522.72-7.72
21419.72-15.72
22317.92-14.92
23617.32-11.32
24514.32-9.32
251212.6-0.599999999999998
261214.6-2.6
271214.8-2.8
2814122
291211.40.600000000000001
301720.4-3.4
311218.6-6.6
322022.72-2.72000000000000
332119.721.28000000000000
341517.92-2.92
352217.324.68
361914.324.68
371912.66.4
382614.611.4
392514.810.2
4019127
412011.48.6
423020.49.6
433118.612.4
443522.7212.28
453319.7213.28
462617.928.08
472517.327.68
481714.322.68000000000000
491412.61.40000000000000
50814.6-6.6
511214.8-2.8
52712-5
53411.4-7.4
541020.4-10.4
55818.6-10.6
561618.12-2.12
571415.12-1.12000000000000
582013.326.68
59912.72-3.72
60109.720.28

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0 & 12.6 & -12.6 \tabularnewline
2 & 9 & 14.6 & -5.6 \tabularnewline
3 & 1 & 14.8 & -13.8 \tabularnewline
4 & 4 & 12 & -8.00000000000001 \tabularnewline
5 & 6 & 11.4 & -5.4 \tabularnewline
6 & 21 & 20.4 & 0.600000000000003 \tabularnewline
7 & 24 & 18.6 & 5.39999999999999 \tabularnewline
8 & 23 & 22.72 & 0.280000000000007 \tabularnewline
9 & 22 & 19.72 & 2.28 \tabularnewline
10 & 21 & 17.92 & 3.07999999999999 \tabularnewline
11 & 20 & 17.32 & 2.67999999999999 \tabularnewline
12 & 16 & 14.32 & 1.68000000000000 \tabularnewline
13 & 18 & 12.6 & 5.4 \tabularnewline
14 & 18 & 14.6 & 3.4 \tabularnewline
15 & 24 & 14.8 & 9.2 \tabularnewline
16 & 16 & 12 & 4 \tabularnewline
17 & 15 & 11.4 & 3.6 \tabularnewline
18 & 24 & 20.4 & 3.6 \tabularnewline
19 & 18 & 18.6 & -0.599999999999998 \tabularnewline
20 & 15 & 22.72 & -7.72 \tabularnewline
21 & 4 & 19.72 & -15.72 \tabularnewline
22 & 3 & 17.92 & -14.92 \tabularnewline
23 & 6 & 17.32 & -11.32 \tabularnewline
24 & 5 & 14.32 & -9.32 \tabularnewline
25 & 12 & 12.6 & -0.599999999999998 \tabularnewline
26 & 12 & 14.6 & -2.6 \tabularnewline
27 & 12 & 14.8 & -2.8 \tabularnewline
28 & 14 & 12 & 2 \tabularnewline
29 & 12 & 11.4 & 0.600000000000001 \tabularnewline
30 & 17 & 20.4 & -3.4 \tabularnewline
31 & 12 & 18.6 & -6.6 \tabularnewline
32 & 20 & 22.72 & -2.72000000000000 \tabularnewline
33 & 21 & 19.72 & 1.28000000000000 \tabularnewline
34 & 15 & 17.92 & -2.92 \tabularnewline
35 & 22 & 17.32 & 4.68 \tabularnewline
36 & 19 & 14.32 & 4.68 \tabularnewline
37 & 19 & 12.6 & 6.4 \tabularnewline
38 & 26 & 14.6 & 11.4 \tabularnewline
39 & 25 & 14.8 & 10.2 \tabularnewline
40 & 19 & 12 & 7 \tabularnewline
41 & 20 & 11.4 & 8.6 \tabularnewline
42 & 30 & 20.4 & 9.6 \tabularnewline
43 & 31 & 18.6 & 12.4 \tabularnewline
44 & 35 & 22.72 & 12.28 \tabularnewline
45 & 33 & 19.72 & 13.28 \tabularnewline
46 & 26 & 17.92 & 8.08 \tabularnewline
47 & 25 & 17.32 & 7.68 \tabularnewline
48 & 17 & 14.32 & 2.68000000000000 \tabularnewline
49 & 14 & 12.6 & 1.40000000000000 \tabularnewline
50 & 8 & 14.6 & -6.6 \tabularnewline
51 & 12 & 14.8 & -2.8 \tabularnewline
52 & 7 & 12 & -5 \tabularnewline
53 & 4 & 11.4 & -7.4 \tabularnewline
54 & 10 & 20.4 & -10.4 \tabularnewline
55 & 8 & 18.6 & -10.6 \tabularnewline
56 & 16 & 18.12 & -2.12 \tabularnewline
57 & 14 & 15.12 & -1.12000000000000 \tabularnewline
58 & 20 & 13.32 & 6.68 \tabularnewline
59 & 9 & 12.72 & -3.72 \tabularnewline
60 & 10 & 9.72 & 0.28 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29342&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]0[/C][C]12.6[/C][C]-12.6[/C][/ROW]
[ROW][C]2[/C][C]9[/C][C]14.6[/C][C]-5.6[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]14.8[/C][C]-13.8[/C][/ROW]
[ROW][C]4[/C][C]4[/C][C]12[/C][C]-8.00000000000001[/C][/ROW]
[ROW][C]5[/C][C]6[/C][C]11.4[/C][C]-5.4[/C][/ROW]
[ROW][C]6[/C][C]21[/C][C]20.4[/C][C]0.600000000000003[/C][/ROW]
[ROW][C]7[/C][C]24[/C][C]18.6[/C][C]5.39999999999999[/C][/ROW]
[ROW][C]8[/C][C]23[/C][C]22.72[/C][C]0.280000000000007[/C][/ROW]
[ROW][C]9[/C][C]22[/C][C]19.72[/C][C]2.28[/C][/ROW]
[ROW][C]10[/C][C]21[/C][C]17.92[/C][C]3.07999999999999[/C][/ROW]
[ROW][C]11[/C][C]20[/C][C]17.32[/C][C]2.67999999999999[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]14.32[/C][C]1.68000000000000[/C][/ROW]
[ROW][C]13[/C][C]18[/C][C]12.6[/C][C]5.4[/C][/ROW]
[ROW][C]14[/C][C]18[/C][C]14.6[/C][C]3.4[/C][/ROW]
[ROW][C]15[/C][C]24[/C][C]14.8[/C][C]9.2[/C][/ROW]
[ROW][C]16[/C][C]16[/C][C]12[/C][C]4[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]11.4[/C][C]3.6[/C][/ROW]
[ROW][C]18[/C][C]24[/C][C]20.4[/C][C]3.6[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]18.6[/C][C]-0.599999999999998[/C][/ROW]
[ROW][C]20[/C][C]15[/C][C]22.72[/C][C]-7.72[/C][/ROW]
[ROW][C]21[/C][C]4[/C][C]19.72[/C][C]-15.72[/C][/ROW]
[ROW][C]22[/C][C]3[/C][C]17.92[/C][C]-14.92[/C][/ROW]
[ROW][C]23[/C][C]6[/C][C]17.32[/C][C]-11.32[/C][/ROW]
[ROW][C]24[/C][C]5[/C][C]14.32[/C][C]-9.32[/C][/ROW]
[ROW][C]25[/C][C]12[/C][C]12.6[/C][C]-0.599999999999998[/C][/ROW]
[ROW][C]26[/C][C]12[/C][C]14.6[/C][C]-2.6[/C][/ROW]
[ROW][C]27[/C][C]12[/C][C]14.8[/C][C]-2.8[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]12[/C][C]2[/C][/ROW]
[ROW][C]29[/C][C]12[/C][C]11.4[/C][C]0.600000000000001[/C][/ROW]
[ROW][C]30[/C][C]17[/C][C]20.4[/C][C]-3.4[/C][/ROW]
[ROW][C]31[/C][C]12[/C][C]18.6[/C][C]-6.6[/C][/ROW]
[ROW][C]32[/C][C]20[/C][C]22.72[/C][C]-2.72000000000000[/C][/ROW]
[ROW][C]33[/C][C]21[/C][C]19.72[/C][C]1.28000000000000[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]17.92[/C][C]-2.92[/C][/ROW]
[ROW][C]35[/C][C]22[/C][C]17.32[/C][C]4.68[/C][/ROW]
[ROW][C]36[/C][C]19[/C][C]14.32[/C][C]4.68[/C][/ROW]
[ROW][C]37[/C][C]19[/C][C]12.6[/C][C]6.4[/C][/ROW]
[ROW][C]38[/C][C]26[/C][C]14.6[/C][C]11.4[/C][/ROW]
[ROW][C]39[/C][C]25[/C][C]14.8[/C][C]10.2[/C][/ROW]
[ROW][C]40[/C][C]19[/C][C]12[/C][C]7[/C][/ROW]
[ROW][C]41[/C][C]20[/C][C]11.4[/C][C]8.6[/C][/ROW]
[ROW][C]42[/C][C]30[/C][C]20.4[/C][C]9.6[/C][/ROW]
[ROW][C]43[/C][C]31[/C][C]18.6[/C][C]12.4[/C][/ROW]
[ROW][C]44[/C][C]35[/C][C]22.72[/C][C]12.28[/C][/ROW]
[ROW][C]45[/C][C]33[/C][C]19.72[/C][C]13.28[/C][/ROW]
[ROW][C]46[/C][C]26[/C][C]17.92[/C][C]8.08[/C][/ROW]
[ROW][C]47[/C][C]25[/C][C]17.32[/C][C]7.68[/C][/ROW]
[ROW][C]48[/C][C]17[/C][C]14.32[/C][C]2.68000000000000[/C][/ROW]
[ROW][C]49[/C][C]14[/C][C]12.6[/C][C]1.40000000000000[/C][/ROW]
[ROW][C]50[/C][C]8[/C][C]14.6[/C][C]-6.6[/C][/ROW]
[ROW][C]51[/C][C]12[/C][C]14.8[/C][C]-2.8[/C][/ROW]
[ROW][C]52[/C][C]7[/C][C]12[/C][C]-5[/C][/ROW]
[ROW][C]53[/C][C]4[/C][C]11.4[/C][C]-7.4[/C][/ROW]
[ROW][C]54[/C][C]10[/C][C]20.4[/C][C]-10.4[/C][/ROW]
[ROW][C]55[/C][C]8[/C][C]18.6[/C][C]-10.6[/C][/ROW]
[ROW][C]56[/C][C]16[/C][C]18.12[/C][C]-2.12[/C][/ROW]
[ROW][C]57[/C][C]14[/C][C]15.12[/C][C]-1.12000000000000[/C][/ROW]
[ROW][C]58[/C][C]20[/C][C]13.32[/C][C]6.68[/C][/ROW]
[ROW][C]59[/C][C]9[/C][C]12.72[/C][C]-3.72[/C][/ROW]
[ROW][C]60[/C][C]10[/C][C]9.72[/C][C]0.28[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29342&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29342&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
1012.6-12.6
2914.6-5.6
3114.8-13.8
4412-8.00000000000001
5611.4-5.4
62120.40.600000000000003
72418.65.39999999999999
82322.720.280000000000007
92219.722.28
102117.923.07999999999999
112017.322.67999999999999
121614.321.68000000000000
131812.65.4
141814.63.4
152414.89.2
1616124
171511.43.6
182420.43.6
191818.6-0.599999999999998
201522.72-7.72
21419.72-15.72
22317.92-14.92
23617.32-11.32
24514.32-9.32
251212.6-0.599999999999998
261214.6-2.6
271214.8-2.8
2814122
291211.40.600000000000001
301720.4-3.4
311218.6-6.6
322022.72-2.72000000000000
332119.721.28000000000000
341517.92-2.92
352217.324.68
361914.324.68
371912.66.4
382614.611.4
392514.810.2
4019127
412011.48.6
423020.49.6
433118.612.4
443522.7212.28
453319.7213.28
462617.928.08
472517.327.68
481714.322.68000000000000
491412.61.40000000000000
50814.6-6.6
511214.8-2.8
52712-5
53411.4-7.4
541020.4-10.4
55818.6-10.6
561618.12-2.12
571415.12-1.12000000000000
582013.326.68
59912.72-3.72
60109.720.28







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.9081238793831990.1837522412336030.0918761206168014
170.8522063708578550.295587258284290.147793629142145
180.7611670609142930.4776658781714130.238832939085707
190.6633073615629370.6733852768741260.336692638437063
200.5953486281658310.8093027436683380.404651371834169
210.7234299659935630.5531400680128750.276570034006437
220.8286908556277170.3426182887445660.171309144372283
230.8604430569607080.2791138860785850.139556943039292
240.8674352956810670.2651294086378670.132564704318933
250.8124461943678640.3751076112642710.187553805632136
260.7452779486190280.5094441027619430.254722051380972
270.6738770762605040.6522458474789930.326122923739496
280.5870969399040460.8258061201919090.412903060095954
290.4893321110537070.9786642221074140.510667888946293
300.4088483349758990.8176966699517990.5911516650241
310.3718955126638070.7437910253276140.628104487336193
320.3255418837961410.6510837675922820.67445811620386
330.2883884484232640.5767768968465270.711611551576737
340.29060989287040.58121978574080.7093901071296
350.2358051098505380.4716102197010770.764194890149462
360.1825856020134860.3651712040269720.817414397986514
370.1449040504651260.2898081009302510.855095949534874
380.2054119688598020.4108239377196040.794588031140198
390.2218769215001370.4437538430002750.778123078499863
400.2030154586968440.4060309173936880.796984541303156
410.2353691065728270.4707382131456530.764630893427173
420.38474441338860.76948882677720.6152555866114
430.8656797152988150.2686405694023710.134320284701185
440.8232106323682380.3535787352635250.176789367631762

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.908123879383199 & 0.183752241233603 & 0.0918761206168014 \tabularnewline
17 & 0.852206370857855 & 0.29558725828429 & 0.147793629142145 \tabularnewline
18 & 0.761167060914293 & 0.477665878171413 & 0.238832939085707 \tabularnewline
19 & 0.663307361562937 & 0.673385276874126 & 0.336692638437063 \tabularnewline
20 & 0.595348628165831 & 0.809302743668338 & 0.404651371834169 \tabularnewline
21 & 0.723429965993563 & 0.553140068012875 & 0.276570034006437 \tabularnewline
22 & 0.828690855627717 & 0.342618288744566 & 0.171309144372283 \tabularnewline
23 & 0.860443056960708 & 0.279113886078585 & 0.139556943039292 \tabularnewline
24 & 0.867435295681067 & 0.265129408637867 & 0.132564704318933 \tabularnewline
25 & 0.812446194367864 & 0.375107611264271 & 0.187553805632136 \tabularnewline
26 & 0.745277948619028 & 0.509444102761943 & 0.254722051380972 \tabularnewline
27 & 0.673877076260504 & 0.652245847478993 & 0.326122923739496 \tabularnewline
28 & 0.587096939904046 & 0.825806120191909 & 0.412903060095954 \tabularnewline
29 & 0.489332111053707 & 0.978664222107414 & 0.510667888946293 \tabularnewline
30 & 0.408848334975899 & 0.817696669951799 & 0.5911516650241 \tabularnewline
31 & 0.371895512663807 & 0.743791025327614 & 0.628104487336193 \tabularnewline
32 & 0.325541883796141 & 0.651083767592282 & 0.67445811620386 \tabularnewline
33 & 0.288388448423264 & 0.576776896846527 & 0.711611551576737 \tabularnewline
34 & 0.2906098928704 & 0.5812197857408 & 0.7093901071296 \tabularnewline
35 & 0.235805109850538 & 0.471610219701077 & 0.764194890149462 \tabularnewline
36 & 0.182585602013486 & 0.365171204026972 & 0.817414397986514 \tabularnewline
37 & 0.144904050465126 & 0.289808100930251 & 0.855095949534874 \tabularnewline
38 & 0.205411968859802 & 0.410823937719604 & 0.794588031140198 \tabularnewline
39 & 0.221876921500137 & 0.443753843000275 & 0.778123078499863 \tabularnewline
40 & 0.203015458696844 & 0.406030917393688 & 0.796984541303156 \tabularnewline
41 & 0.235369106572827 & 0.470738213145653 & 0.764630893427173 \tabularnewline
42 & 0.3847444133886 & 0.7694888267772 & 0.6152555866114 \tabularnewline
43 & 0.865679715298815 & 0.268640569402371 & 0.134320284701185 \tabularnewline
44 & 0.823210632368238 & 0.353578735263525 & 0.176789367631762 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29342&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]16[/C][C]0.908123879383199[/C][C]0.183752241233603[/C][C]0.0918761206168014[/C][/ROW]
[ROW][C]17[/C][C]0.852206370857855[/C][C]0.29558725828429[/C][C]0.147793629142145[/C][/ROW]
[ROW][C]18[/C][C]0.761167060914293[/C][C]0.477665878171413[/C][C]0.238832939085707[/C][/ROW]
[ROW][C]19[/C][C]0.663307361562937[/C][C]0.673385276874126[/C][C]0.336692638437063[/C][/ROW]
[ROW][C]20[/C][C]0.595348628165831[/C][C]0.809302743668338[/C][C]0.404651371834169[/C][/ROW]
[ROW][C]21[/C][C]0.723429965993563[/C][C]0.553140068012875[/C][C]0.276570034006437[/C][/ROW]
[ROW][C]22[/C][C]0.828690855627717[/C][C]0.342618288744566[/C][C]0.171309144372283[/C][/ROW]
[ROW][C]23[/C][C]0.860443056960708[/C][C]0.279113886078585[/C][C]0.139556943039292[/C][/ROW]
[ROW][C]24[/C][C]0.867435295681067[/C][C]0.265129408637867[/C][C]0.132564704318933[/C][/ROW]
[ROW][C]25[/C][C]0.812446194367864[/C][C]0.375107611264271[/C][C]0.187553805632136[/C][/ROW]
[ROW][C]26[/C][C]0.745277948619028[/C][C]0.509444102761943[/C][C]0.254722051380972[/C][/ROW]
[ROW][C]27[/C][C]0.673877076260504[/C][C]0.652245847478993[/C][C]0.326122923739496[/C][/ROW]
[ROW][C]28[/C][C]0.587096939904046[/C][C]0.825806120191909[/C][C]0.412903060095954[/C][/ROW]
[ROW][C]29[/C][C]0.489332111053707[/C][C]0.978664222107414[/C][C]0.510667888946293[/C][/ROW]
[ROW][C]30[/C][C]0.408848334975899[/C][C]0.817696669951799[/C][C]0.5911516650241[/C][/ROW]
[ROW][C]31[/C][C]0.371895512663807[/C][C]0.743791025327614[/C][C]0.628104487336193[/C][/ROW]
[ROW][C]32[/C][C]0.325541883796141[/C][C]0.651083767592282[/C][C]0.67445811620386[/C][/ROW]
[ROW][C]33[/C][C]0.288388448423264[/C][C]0.576776896846527[/C][C]0.711611551576737[/C][/ROW]
[ROW][C]34[/C][C]0.2906098928704[/C][C]0.5812197857408[/C][C]0.7093901071296[/C][/ROW]
[ROW][C]35[/C][C]0.235805109850538[/C][C]0.471610219701077[/C][C]0.764194890149462[/C][/ROW]
[ROW][C]36[/C][C]0.182585602013486[/C][C]0.365171204026972[/C][C]0.817414397986514[/C][/ROW]
[ROW][C]37[/C][C]0.144904050465126[/C][C]0.289808100930251[/C][C]0.855095949534874[/C][/ROW]
[ROW][C]38[/C][C]0.205411968859802[/C][C]0.410823937719604[/C][C]0.794588031140198[/C][/ROW]
[ROW][C]39[/C][C]0.221876921500137[/C][C]0.443753843000275[/C][C]0.778123078499863[/C][/ROW]
[ROW][C]40[/C][C]0.203015458696844[/C][C]0.406030917393688[/C][C]0.796984541303156[/C][/ROW]
[ROW][C]41[/C][C]0.235369106572827[/C][C]0.470738213145653[/C][C]0.764630893427173[/C][/ROW]
[ROW][C]42[/C][C]0.3847444133886[/C][C]0.7694888267772[/C][C]0.6152555866114[/C][/ROW]
[ROW][C]43[/C][C]0.865679715298815[/C][C]0.268640569402371[/C][C]0.134320284701185[/C][/ROW]
[ROW][C]44[/C][C]0.823210632368238[/C][C]0.353578735263525[/C][C]0.176789367631762[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29342&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29342&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
160.9081238793831990.1837522412336030.0918761206168014
170.8522063708578550.295587258284290.147793629142145
180.7611670609142930.4776658781714130.238832939085707
190.6633073615629370.6733852768741260.336692638437063
200.5953486281658310.8093027436683380.404651371834169
210.7234299659935630.5531400680128750.276570034006437
220.8286908556277170.3426182887445660.171309144372283
230.8604430569607080.2791138860785850.139556943039292
240.8674352956810670.2651294086378670.132564704318933
250.8124461943678640.3751076112642710.187553805632136
260.7452779486190280.5094441027619430.254722051380972
270.6738770762605040.6522458474789930.326122923739496
280.5870969399040460.8258061201919090.412903060095954
290.4893321110537070.9786642221074140.510667888946293
300.4088483349758990.8176966699517990.5911516650241
310.3718955126638070.7437910253276140.628104487336193
320.3255418837961410.6510837675922820.67445811620386
330.2883884484232640.5767768968465270.711611551576737
340.29060989287040.58121978574080.7093901071296
350.2358051098505380.4716102197010770.764194890149462
360.1825856020134860.3651712040269720.817414397986514
370.1449040504651260.2898081009302510.855095949534874
380.2054119688598020.4108239377196040.794588031140198
390.2218769215001370.4437538430002750.778123078499863
400.2030154586968440.4060309173936880.796984541303156
410.2353691065728270.4707382131456530.764630893427173
420.38474441338860.76948882677720.6152555866114
430.8656797152988150.2686405694023710.134320284701185
440.8232106323682380.3535787352635250.176789367631762







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=29342&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=29342&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29342&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 level00OK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly 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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], 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,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
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, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
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,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
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,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
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,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
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,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
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')
}