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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 computationThu, 19 Nov 2009 14:28:45 -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/2009/Nov/19/t1258666914qls8c9sirj027my.htm/, Retrieved Fri, 29 Mar 2024 13:16:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57966, Retrieved Fri, 29 Mar 2024 13:16:33 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact128
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [] [2009-11-19 21:28:45] [90c9838c596c9c0a7d0d4c412ffe5b98] [Current]
- R  D        [Multiple Regression] [] [2009-11-20 18:00:29] [859f65298c93b90426725427c75f8582]
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Dataseries X:
6802,96	0
7132,68	0
7073,29	0
7264,5	0
7105,33	0
7218,71	0
7225,72	0
7354,25	0
7745,46	0
8070,26	0
8366,33	0
8667,51	0
8854,34	0
9218,1	0
9332,9	0
9358,31	0
9248,66	0
9401,2	0
9652,04	0
9957,38	0
10110,63	0
10169,26	0
10343,78	0
10750,21	0
11337,5	0
11786,96	0
12083,04	0
12007,74	0
11745,93	0
11051,51	0
11445,9	0
11924,88	0
12247,63	0
12690,91	0
12910,7	0
13202,12	0
13654,67	0
13862,82	0
13523,93	0
14211,17	0
14510,35	0
14289,23	0
14111,82	0
13086,59	0
13351,54	0
13747,69	0
12855,61	0
12926,93	0
12121,95	1
11731,65	1
11639,51	1
12163,78	1
12029,53	1
11234,18	1
9852,13	1
9709,04	1
9332,75	1
7108,6	1
6691,49	1
6143,05	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57966&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57966&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57966&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 10728.97875 -749.17375X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  10728.97875 -749.17375X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57966&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  10728.97875 -749.17375X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57966&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57966&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
Y[t] = + 10728.97875 -749.17375X[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10728.97875345.72627131.033200
X-749.17375773.067444-0.96910.3365230.168262

\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) & 10728.97875 & 345.726271 & 31.0332 & 0 & 0 \tabularnewline
X & -749.17375 & 773.067444 & -0.9691 & 0.336523 & 0.168262 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57966&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]10728.97875[/C][C]345.726271[/C][C]31.0332[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-749.17375[/C][C]773.067444[/C][C]-0.9691[/C][C]0.336523[/C][C]0.168262[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57966&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57966&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)10728.97875345.72627131.033200
X-749.17375773.067444-0.96910.3365230.168262







Multiple Linear Regression - Regression Statistics
Multiple R0.126230200070555
R-squared0.0159340634098522
Adjusted R-squared-0.00103259066928807
F-TEST (value)0.939139993986336
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0.336523066256252
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2395.2618673994
Sum Squared Residuals332762205.978225

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.126230200070555 \tabularnewline
R-squared & 0.0159340634098522 \tabularnewline
Adjusted R-squared & -0.00103259066928807 \tabularnewline
F-TEST (value) & 0.939139993986336 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0.336523066256252 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2395.2618673994 \tabularnewline
Sum Squared Residuals & 332762205.978225 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57966&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.126230200070555[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0159340634098522[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.00103259066928807[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.939139993986336[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]0.336523066256252[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2395.2618673994[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]332762205.978225[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57966&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57966&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 R0.126230200070555
R-squared0.0159340634098522
Adjusted R-squared-0.00103259066928807
F-TEST (value)0.939139993986336
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0.336523066256252
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2395.2618673994
Sum Squared Residuals332762205.978225







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16802.9610728.97875-3926.01875000001
27132.6810728.97875-3596.29875
37073.2910728.97875-3655.68875
47264.510728.97875-3464.47875
57105.3310728.97875-3623.64875
67218.7110728.97875-3510.26875
77225.7210728.97875-3503.25875
87354.2510728.97875-3374.72875
97745.4610728.97875-2983.51875
108070.2610728.97875-2658.71875
118366.3310728.97875-2362.64875
128667.5110728.97875-2061.46875
138854.3410728.97875-1874.63875
149218.110728.97875-1510.87875
159332.910728.97875-1396.07875
169358.3110728.97875-1370.66875
179248.6610728.97875-1480.31875
189401.210728.97875-1327.77875
199652.0410728.97875-1076.93875
209957.3810728.97875-771.59875
2110110.6310728.97875-618.34875
2210169.2610728.97875-559.71875
2310343.7810728.97875-385.198749999999
2410750.2110728.9787521.2312499999993
2511337.510728.97875608.52125
2611786.9610728.978751057.98125
2712083.0410728.978751354.06125
2812007.7410728.978751278.76125
2911745.9310728.978751016.95125
3011051.5110728.97875322.531250000000
3111445.910728.97875716.92125
3211924.8810728.978751195.90125
3312247.6310728.978751518.65125
3412690.9110728.978751961.93125
3512910.710728.978752181.72125
3613202.1210728.978752473.14125
3713654.6710728.978752925.69125
3813862.8210728.978753133.84125
3913523.9310728.978752794.95125
4014211.1710728.978753482.19125
4114510.3510728.978753781.37125
4214289.2310728.978753560.25125
4314111.8210728.978753382.84125
4413086.5910728.978752357.61125
4513351.5410728.978752622.56125
4613747.6910728.978753018.71125
4712855.6110728.978752126.63125
4812926.9310728.978752197.95125
4912121.959979.8052142.145
5011731.659979.8051751.845
5111639.519979.8051659.705
5212163.789979.8052183.975
5312029.539979.8052049.725
5411234.189979.8051254.375
559852.139979.805-127.675000000001
569709.049979.805-270.764999999999
579332.759979.805-647.055
587108.69979.805-2871.205
596691.499979.805-3288.315
606143.059979.805-3836.755

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 6802.96 & 10728.97875 & -3926.01875000001 \tabularnewline
2 & 7132.68 & 10728.97875 & -3596.29875 \tabularnewline
3 & 7073.29 & 10728.97875 & -3655.68875 \tabularnewline
4 & 7264.5 & 10728.97875 & -3464.47875 \tabularnewline
5 & 7105.33 & 10728.97875 & -3623.64875 \tabularnewline
6 & 7218.71 & 10728.97875 & -3510.26875 \tabularnewline
7 & 7225.72 & 10728.97875 & -3503.25875 \tabularnewline
8 & 7354.25 & 10728.97875 & -3374.72875 \tabularnewline
9 & 7745.46 & 10728.97875 & -2983.51875 \tabularnewline
10 & 8070.26 & 10728.97875 & -2658.71875 \tabularnewline
11 & 8366.33 & 10728.97875 & -2362.64875 \tabularnewline
12 & 8667.51 & 10728.97875 & -2061.46875 \tabularnewline
13 & 8854.34 & 10728.97875 & -1874.63875 \tabularnewline
14 & 9218.1 & 10728.97875 & -1510.87875 \tabularnewline
15 & 9332.9 & 10728.97875 & -1396.07875 \tabularnewline
16 & 9358.31 & 10728.97875 & -1370.66875 \tabularnewline
17 & 9248.66 & 10728.97875 & -1480.31875 \tabularnewline
18 & 9401.2 & 10728.97875 & -1327.77875 \tabularnewline
19 & 9652.04 & 10728.97875 & -1076.93875 \tabularnewline
20 & 9957.38 & 10728.97875 & -771.59875 \tabularnewline
21 & 10110.63 & 10728.97875 & -618.34875 \tabularnewline
22 & 10169.26 & 10728.97875 & -559.71875 \tabularnewline
23 & 10343.78 & 10728.97875 & -385.198749999999 \tabularnewline
24 & 10750.21 & 10728.97875 & 21.2312499999993 \tabularnewline
25 & 11337.5 & 10728.97875 & 608.52125 \tabularnewline
26 & 11786.96 & 10728.97875 & 1057.98125 \tabularnewline
27 & 12083.04 & 10728.97875 & 1354.06125 \tabularnewline
28 & 12007.74 & 10728.97875 & 1278.76125 \tabularnewline
29 & 11745.93 & 10728.97875 & 1016.95125 \tabularnewline
30 & 11051.51 & 10728.97875 & 322.531250000000 \tabularnewline
31 & 11445.9 & 10728.97875 & 716.92125 \tabularnewline
32 & 11924.88 & 10728.97875 & 1195.90125 \tabularnewline
33 & 12247.63 & 10728.97875 & 1518.65125 \tabularnewline
34 & 12690.91 & 10728.97875 & 1961.93125 \tabularnewline
35 & 12910.7 & 10728.97875 & 2181.72125 \tabularnewline
36 & 13202.12 & 10728.97875 & 2473.14125 \tabularnewline
37 & 13654.67 & 10728.97875 & 2925.69125 \tabularnewline
38 & 13862.82 & 10728.97875 & 3133.84125 \tabularnewline
39 & 13523.93 & 10728.97875 & 2794.95125 \tabularnewline
40 & 14211.17 & 10728.97875 & 3482.19125 \tabularnewline
41 & 14510.35 & 10728.97875 & 3781.37125 \tabularnewline
42 & 14289.23 & 10728.97875 & 3560.25125 \tabularnewline
43 & 14111.82 & 10728.97875 & 3382.84125 \tabularnewline
44 & 13086.59 & 10728.97875 & 2357.61125 \tabularnewline
45 & 13351.54 & 10728.97875 & 2622.56125 \tabularnewline
46 & 13747.69 & 10728.97875 & 3018.71125 \tabularnewline
47 & 12855.61 & 10728.97875 & 2126.63125 \tabularnewline
48 & 12926.93 & 10728.97875 & 2197.95125 \tabularnewline
49 & 12121.95 & 9979.805 & 2142.145 \tabularnewline
50 & 11731.65 & 9979.805 & 1751.845 \tabularnewline
51 & 11639.51 & 9979.805 & 1659.705 \tabularnewline
52 & 12163.78 & 9979.805 & 2183.975 \tabularnewline
53 & 12029.53 & 9979.805 & 2049.725 \tabularnewline
54 & 11234.18 & 9979.805 & 1254.375 \tabularnewline
55 & 9852.13 & 9979.805 & -127.675000000001 \tabularnewline
56 & 9709.04 & 9979.805 & -270.764999999999 \tabularnewline
57 & 9332.75 & 9979.805 & -647.055 \tabularnewline
58 & 7108.6 & 9979.805 & -2871.205 \tabularnewline
59 & 6691.49 & 9979.805 & -3288.315 \tabularnewline
60 & 6143.05 & 9979.805 & -3836.755 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57966&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]6802.96[/C][C]10728.97875[/C][C]-3926.01875000001[/C][/ROW]
[ROW][C]2[/C][C]7132.68[/C][C]10728.97875[/C][C]-3596.29875[/C][/ROW]
[ROW][C]3[/C][C]7073.29[/C][C]10728.97875[/C][C]-3655.68875[/C][/ROW]
[ROW][C]4[/C][C]7264.5[/C][C]10728.97875[/C][C]-3464.47875[/C][/ROW]
[ROW][C]5[/C][C]7105.33[/C][C]10728.97875[/C][C]-3623.64875[/C][/ROW]
[ROW][C]6[/C][C]7218.71[/C][C]10728.97875[/C][C]-3510.26875[/C][/ROW]
[ROW][C]7[/C][C]7225.72[/C][C]10728.97875[/C][C]-3503.25875[/C][/ROW]
[ROW][C]8[/C][C]7354.25[/C][C]10728.97875[/C][C]-3374.72875[/C][/ROW]
[ROW][C]9[/C][C]7745.46[/C][C]10728.97875[/C][C]-2983.51875[/C][/ROW]
[ROW][C]10[/C][C]8070.26[/C][C]10728.97875[/C][C]-2658.71875[/C][/ROW]
[ROW][C]11[/C][C]8366.33[/C][C]10728.97875[/C][C]-2362.64875[/C][/ROW]
[ROW][C]12[/C][C]8667.51[/C][C]10728.97875[/C][C]-2061.46875[/C][/ROW]
[ROW][C]13[/C][C]8854.34[/C][C]10728.97875[/C][C]-1874.63875[/C][/ROW]
[ROW][C]14[/C][C]9218.1[/C][C]10728.97875[/C][C]-1510.87875[/C][/ROW]
[ROW][C]15[/C][C]9332.9[/C][C]10728.97875[/C][C]-1396.07875[/C][/ROW]
[ROW][C]16[/C][C]9358.31[/C][C]10728.97875[/C][C]-1370.66875[/C][/ROW]
[ROW][C]17[/C][C]9248.66[/C][C]10728.97875[/C][C]-1480.31875[/C][/ROW]
[ROW][C]18[/C][C]9401.2[/C][C]10728.97875[/C][C]-1327.77875[/C][/ROW]
[ROW][C]19[/C][C]9652.04[/C][C]10728.97875[/C][C]-1076.93875[/C][/ROW]
[ROW][C]20[/C][C]9957.38[/C][C]10728.97875[/C][C]-771.59875[/C][/ROW]
[ROW][C]21[/C][C]10110.63[/C][C]10728.97875[/C][C]-618.34875[/C][/ROW]
[ROW][C]22[/C][C]10169.26[/C][C]10728.97875[/C][C]-559.71875[/C][/ROW]
[ROW][C]23[/C][C]10343.78[/C][C]10728.97875[/C][C]-385.198749999999[/C][/ROW]
[ROW][C]24[/C][C]10750.21[/C][C]10728.97875[/C][C]21.2312499999993[/C][/ROW]
[ROW][C]25[/C][C]11337.5[/C][C]10728.97875[/C][C]608.52125[/C][/ROW]
[ROW][C]26[/C][C]11786.96[/C][C]10728.97875[/C][C]1057.98125[/C][/ROW]
[ROW][C]27[/C][C]12083.04[/C][C]10728.97875[/C][C]1354.06125[/C][/ROW]
[ROW][C]28[/C][C]12007.74[/C][C]10728.97875[/C][C]1278.76125[/C][/ROW]
[ROW][C]29[/C][C]11745.93[/C][C]10728.97875[/C][C]1016.95125[/C][/ROW]
[ROW][C]30[/C][C]11051.51[/C][C]10728.97875[/C][C]322.531250000000[/C][/ROW]
[ROW][C]31[/C][C]11445.9[/C][C]10728.97875[/C][C]716.92125[/C][/ROW]
[ROW][C]32[/C][C]11924.88[/C][C]10728.97875[/C][C]1195.90125[/C][/ROW]
[ROW][C]33[/C][C]12247.63[/C][C]10728.97875[/C][C]1518.65125[/C][/ROW]
[ROW][C]34[/C][C]12690.91[/C][C]10728.97875[/C][C]1961.93125[/C][/ROW]
[ROW][C]35[/C][C]12910.7[/C][C]10728.97875[/C][C]2181.72125[/C][/ROW]
[ROW][C]36[/C][C]13202.12[/C][C]10728.97875[/C][C]2473.14125[/C][/ROW]
[ROW][C]37[/C][C]13654.67[/C][C]10728.97875[/C][C]2925.69125[/C][/ROW]
[ROW][C]38[/C][C]13862.82[/C][C]10728.97875[/C][C]3133.84125[/C][/ROW]
[ROW][C]39[/C][C]13523.93[/C][C]10728.97875[/C][C]2794.95125[/C][/ROW]
[ROW][C]40[/C][C]14211.17[/C][C]10728.97875[/C][C]3482.19125[/C][/ROW]
[ROW][C]41[/C][C]14510.35[/C][C]10728.97875[/C][C]3781.37125[/C][/ROW]
[ROW][C]42[/C][C]14289.23[/C][C]10728.97875[/C][C]3560.25125[/C][/ROW]
[ROW][C]43[/C][C]14111.82[/C][C]10728.97875[/C][C]3382.84125[/C][/ROW]
[ROW][C]44[/C][C]13086.59[/C][C]10728.97875[/C][C]2357.61125[/C][/ROW]
[ROW][C]45[/C][C]13351.54[/C][C]10728.97875[/C][C]2622.56125[/C][/ROW]
[ROW][C]46[/C][C]13747.69[/C][C]10728.97875[/C][C]3018.71125[/C][/ROW]
[ROW][C]47[/C][C]12855.61[/C][C]10728.97875[/C][C]2126.63125[/C][/ROW]
[ROW][C]48[/C][C]12926.93[/C][C]10728.97875[/C][C]2197.95125[/C][/ROW]
[ROW][C]49[/C][C]12121.95[/C][C]9979.805[/C][C]2142.145[/C][/ROW]
[ROW][C]50[/C][C]11731.65[/C][C]9979.805[/C][C]1751.845[/C][/ROW]
[ROW][C]51[/C][C]11639.51[/C][C]9979.805[/C][C]1659.705[/C][/ROW]
[ROW][C]52[/C][C]12163.78[/C][C]9979.805[/C][C]2183.975[/C][/ROW]
[ROW][C]53[/C][C]12029.53[/C][C]9979.805[/C][C]2049.725[/C][/ROW]
[ROW][C]54[/C][C]11234.18[/C][C]9979.805[/C][C]1254.375[/C][/ROW]
[ROW][C]55[/C][C]9852.13[/C][C]9979.805[/C][C]-127.675000000001[/C][/ROW]
[ROW][C]56[/C][C]9709.04[/C][C]9979.805[/C][C]-270.764999999999[/C][/ROW]
[ROW][C]57[/C][C]9332.75[/C][C]9979.805[/C][C]-647.055[/C][/ROW]
[ROW][C]58[/C][C]7108.6[/C][C]9979.805[/C][C]-2871.205[/C][/ROW]
[ROW][C]59[/C][C]6691.49[/C][C]9979.805[/C][C]-3288.315[/C][/ROW]
[ROW][C]60[/C][C]6143.05[/C][C]9979.805[/C][C]-3836.755[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57966&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57966&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
16802.9610728.97875-3926.01875000001
27132.6810728.97875-3596.29875
37073.2910728.97875-3655.68875
47264.510728.97875-3464.47875
57105.3310728.97875-3623.64875
67218.7110728.97875-3510.26875
77225.7210728.97875-3503.25875
87354.2510728.97875-3374.72875
97745.4610728.97875-2983.51875
108070.2610728.97875-2658.71875
118366.3310728.97875-2362.64875
128667.5110728.97875-2061.46875
138854.3410728.97875-1874.63875
149218.110728.97875-1510.87875
159332.910728.97875-1396.07875
169358.3110728.97875-1370.66875
179248.6610728.97875-1480.31875
189401.210728.97875-1327.77875
199652.0410728.97875-1076.93875
209957.3810728.97875-771.59875
2110110.6310728.97875-618.34875
2210169.2610728.97875-559.71875
2310343.7810728.97875-385.198749999999
2410750.2110728.9787521.2312499999993
2511337.510728.97875608.52125
2611786.9610728.978751057.98125
2712083.0410728.978751354.06125
2812007.7410728.978751278.76125
2911745.9310728.978751016.95125
3011051.5110728.97875322.531250000000
3111445.910728.97875716.92125
3211924.8810728.978751195.90125
3312247.6310728.978751518.65125
3412690.9110728.978751961.93125
3512910.710728.978752181.72125
3613202.1210728.978752473.14125
3713654.6710728.978752925.69125
3813862.8210728.978753133.84125
3913523.9310728.978752794.95125
4014211.1710728.978753482.19125
4114510.3510728.978753781.37125
4214289.2310728.978753560.25125
4314111.8210728.978753382.84125
4413086.5910728.978752357.61125
4513351.5410728.978752622.56125
4613747.6910728.978753018.71125
4712855.6110728.978752126.63125
4812926.9310728.978752197.95125
4912121.959979.8052142.145
5011731.659979.8051751.845
5111639.519979.8051659.705
5212163.789979.8052183.975
5312029.539979.8052049.725
5411234.189979.8051254.375
559852.139979.805-127.675000000001
569709.049979.805-270.764999999999
579332.759979.805-647.055
587108.69979.805-2871.205
596691.499979.805-3288.315
606143.059979.805-3836.755







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.0009605878267575660.001921175653515130.999039412173242
60.0001018646598754990.0002037293197509980.999898135340125
71.09268826022719e-052.18537652045439e-050.999989073117398
82.27519999069831e-064.55039998139663e-060.99999772480001
96.4677403364485e-061.2935480672897e-050.999993532259664
102.56278152715428e-055.12556305430856e-050.999974372184729
118.90142765236393e-050.0001780285530472790.999910985723476
120.0002892207540861410.0005784415081722830.999710779245914
130.0006964144289536680.001392828857907340.999303585571046
140.001973280306222310.003946560612444620.998026719693778
150.004087999348975840.008175998697951680.995912000651024
160.006669988168185660.01333997633637130.993330011831814
170.008866396392229720.01773279278445940.99113360360777
180.01257178590918670.02514357181837340.987428214090813
190.0197667490556240.0395334981112480.980233250944376
200.03390332365017120.06780664730034250.966096676349829
210.05580622592986410.1116124518597280.944193774070136
220.0854997641212590.1709995282425180.914500235878741
230.1283809696207080.2567619392414160.871619030379292
240.1958153953364830.3916307906729650.804184604663517
250.3011101459266060.6022202918532110.698889854073395
260.427729913881620.855459827763240.57227008611838
270.5458294808861350.908341038227730.454170519113865
280.6226776979300940.7546446041398120.377322302069906
290.6665390471200850.666921905759830.333460952879915
300.7003545805362590.5992908389274820.299645419463741
310.7323835324384550.5352329351230910.267616467561545
320.7616953568334410.4766092863331180.238304643166559
330.7867369179376340.4265261641247320.213263082062366
340.8091647864108990.3816704271782020.190835213589101
350.825014414552070.3499711708958620.174985585447931
360.836837811796630.326324376406740.16316218820337
370.8498209262286960.3003581475426070.150179073771304
380.8581271157156580.2837457685686840.141872884284342
390.8499506684096540.3000986631806910.150049331590346
400.8528644595598950.294271080880210.147135540440105
410.8591414911151260.2817170177697490.140858508884874
420.8521399510089590.2957200979820820.147860048991041
430.8352926305045610.3294147389908780.164707369495439
440.7899137538460990.4201724923078020.210086246153901
450.7385331168792620.5229337662414770.261466883120738
460.6897000006869460.6205999986261070.310299999313054
470.6123282472312610.7753435055374780.387671752768739
480.5272396555652470.9455206888695070.472760344434753
490.4960083079595120.9920166159190250.503991692040488
500.451528500798820.903057001597640.54847149920118
510.4135083654551710.8270167309103420.586491634544829
520.4503204871772320.9006409743544640.549679512822768
530.5450244554051070.9099510891897870.454975544594893
540.627581395811440.744837208377120.37241860418856
550.5829921889061590.8340156221876830.417007811093841

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.000960587826757566 & 0.00192117565351513 & 0.999039412173242 \tabularnewline
6 & 0.000101864659875499 & 0.000203729319750998 & 0.999898135340125 \tabularnewline
7 & 1.09268826022719e-05 & 2.18537652045439e-05 & 0.999989073117398 \tabularnewline
8 & 2.27519999069831e-06 & 4.55039998139663e-06 & 0.99999772480001 \tabularnewline
9 & 6.4677403364485e-06 & 1.2935480672897e-05 & 0.999993532259664 \tabularnewline
10 & 2.56278152715428e-05 & 5.12556305430856e-05 & 0.999974372184729 \tabularnewline
11 & 8.90142765236393e-05 & 0.000178028553047279 & 0.999910985723476 \tabularnewline
12 & 0.000289220754086141 & 0.000578441508172283 & 0.999710779245914 \tabularnewline
13 & 0.000696414428953668 & 0.00139282885790734 & 0.999303585571046 \tabularnewline
14 & 0.00197328030622231 & 0.00394656061244462 & 0.998026719693778 \tabularnewline
15 & 0.00408799934897584 & 0.00817599869795168 & 0.995912000651024 \tabularnewline
16 & 0.00666998816818566 & 0.0133399763363713 & 0.993330011831814 \tabularnewline
17 & 0.00886639639222972 & 0.0177327927844594 & 0.99113360360777 \tabularnewline
18 & 0.0125717859091867 & 0.0251435718183734 & 0.987428214090813 \tabularnewline
19 & 0.019766749055624 & 0.039533498111248 & 0.980233250944376 \tabularnewline
20 & 0.0339033236501712 & 0.0678066473003425 & 0.966096676349829 \tabularnewline
21 & 0.0558062259298641 & 0.111612451859728 & 0.944193774070136 \tabularnewline
22 & 0.085499764121259 & 0.170999528242518 & 0.914500235878741 \tabularnewline
23 & 0.128380969620708 & 0.256761939241416 & 0.871619030379292 \tabularnewline
24 & 0.195815395336483 & 0.391630790672965 & 0.804184604663517 \tabularnewline
25 & 0.301110145926606 & 0.602220291853211 & 0.698889854073395 \tabularnewline
26 & 0.42772991388162 & 0.85545982776324 & 0.57227008611838 \tabularnewline
27 & 0.545829480886135 & 0.90834103822773 & 0.454170519113865 \tabularnewline
28 & 0.622677697930094 & 0.754644604139812 & 0.377322302069906 \tabularnewline
29 & 0.666539047120085 & 0.66692190575983 & 0.333460952879915 \tabularnewline
30 & 0.700354580536259 & 0.599290838927482 & 0.299645419463741 \tabularnewline
31 & 0.732383532438455 & 0.535232935123091 & 0.267616467561545 \tabularnewline
32 & 0.761695356833441 & 0.476609286333118 & 0.238304643166559 \tabularnewline
33 & 0.786736917937634 & 0.426526164124732 & 0.213263082062366 \tabularnewline
34 & 0.809164786410899 & 0.381670427178202 & 0.190835213589101 \tabularnewline
35 & 0.82501441455207 & 0.349971170895862 & 0.174985585447931 \tabularnewline
36 & 0.83683781179663 & 0.32632437640674 & 0.16316218820337 \tabularnewline
37 & 0.849820926228696 & 0.300358147542607 & 0.150179073771304 \tabularnewline
38 & 0.858127115715658 & 0.283745768568684 & 0.141872884284342 \tabularnewline
39 & 0.849950668409654 & 0.300098663180691 & 0.150049331590346 \tabularnewline
40 & 0.852864459559895 & 0.29427108088021 & 0.147135540440105 \tabularnewline
41 & 0.859141491115126 & 0.281717017769749 & 0.140858508884874 \tabularnewline
42 & 0.852139951008959 & 0.295720097982082 & 0.147860048991041 \tabularnewline
43 & 0.835292630504561 & 0.329414738990878 & 0.164707369495439 \tabularnewline
44 & 0.789913753846099 & 0.420172492307802 & 0.210086246153901 \tabularnewline
45 & 0.738533116879262 & 0.522933766241477 & 0.261466883120738 \tabularnewline
46 & 0.689700000686946 & 0.620599998626107 & 0.310299999313054 \tabularnewline
47 & 0.612328247231261 & 0.775343505537478 & 0.387671752768739 \tabularnewline
48 & 0.527239655565247 & 0.945520688869507 & 0.472760344434753 \tabularnewline
49 & 0.496008307959512 & 0.992016615919025 & 0.503991692040488 \tabularnewline
50 & 0.45152850079882 & 0.90305700159764 & 0.54847149920118 \tabularnewline
51 & 0.413508365455171 & 0.827016730910342 & 0.586491634544829 \tabularnewline
52 & 0.450320487177232 & 0.900640974354464 & 0.549679512822768 \tabularnewline
53 & 0.545024455405107 & 0.909951089189787 & 0.454975544594893 \tabularnewline
54 & 0.62758139581144 & 0.74483720837712 & 0.37241860418856 \tabularnewline
55 & 0.582992188906159 & 0.834015622187683 & 0.417007811093841 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57966&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]5[/C][C]0.000960587826757566[/C][C]0.00192117565351513[/C][C]0.999039412173242[/C][/ROW]
[ROW][C]6[/C][C]0.000101864659875499[/C][C]0.000203729319750998[/C][C]0.999898135340125[/C][/ROW]
[ROW][C]7[/C][C]1.09268826022719e-05[/C][C]2.18537652045439e-05[/C][C]0.999989073117398[/C][/ROW]
[ROW][C]8[/C][C]2.27519999069831e-06[/C][C]4.55039998139663e-06[/C][C]0.99999772480001[/C][/ROW]
[ROW][C]9[/C][C]6.4677403364485e-06[/C][C]1.2935480672897e-05[/C][C]0.999993532259664[/C][/ROW]
[ROW][C]10[/C][C]2.56278152715428e-05[/C][C]5.12556305430856e-05[/C][C]0.999974372184729[/C][/ROW]
[ROW][C]11[/C][C]8.90142765236393e-05[/C][C]0.000178028553047279[/C][C]0.999910985723476[/C][/ROW]
[ROW][C]12[/C][C]0.000289220754086141[/C][C]0.000578441508172283[/C][C]0.999710779245914[/C][/ROW]
[ROW][C]13[/C][C]0.000696414428953668[/C][C]0.00139282885790734[/C][C]0.999303585571046[/C][/ROW]
[ROW][C]14[/C][C]0.00197328030622231[/C][C]0.00394656061244462[/C][C]0.998026719693778[/C][/ROW]
[ROW][C]15[/C][C]0.00408799934897584[/C][C]0.00817599869795168[/C][C]0.995912000651024[/C][/ROW]
[ROW][C]16[/C][C]0.00666998816818566[/C][C]0.0133399763363713[/C][C]0.993330011831814[/C][/ROW]
[ROW][C]17[/C][C]0.00886639639222972[/C][C]0.0177327927844594[/C][C]0.99113360360777[/C][/ROW]
[ROW][C]18[/C][C]0.0125717859091867[/C][C]0.0251435718183734[/C][C]0.987428214090813[/C][/ROW]
[ROW][C]19[/C][C]0.019766749055624[/C][C]0.039533498111248[/C][C]0.980233250944376[/C][/ROW]
[ROW][C]20[/C][C]0.0339033236501712[/C][C]0.0678066473003425[/C][C]0.966096676349829[/C][/ROW]
[ROW][C]21[/C][C]0.0558062259298641[/C][C]0.111612451859728[/C][C]0.944193774070136[/C][/ROW]
[ROW][C]22[/C][C]0.085499764121259[/C][C]0.170999528242518[/C][C]0.914500235878741[/C][/ROW]
[ROW][C]23[/C][C]0.128380969620708[/C][C]0.256761939241416[/C][C]0.871619030379292[/C][/ROW]
[ROW][C]24[/C][C]0.195815395336483[/C][C]0.391630790672965[/C][C]0.804184604663517[/C][/ROW]
[ROW][C]25[/C][C]0.301110145926606[/C][C]0.602220291853211[/C][C]0.698889854073395[/C][/ROW]
[ROW][C]26[/C][C]0.42772991388162[/C][C]0.85545982776324[/C][C]0.57227008611838[/C][/ROW]
[ROW][C]27[/C][C]0.545829480886135[/C][C]0.90834103822773[/C][C]0.454170519113865[/C][/ROW]
[ROW][C]28[/C][C]0.622677697930094[/C][C]0.754644604139812[/C][C]0.377322302069906[/C][/ROW]
[ROW][C]29[/C][C]0.666539047120085[/C][C]0.66692190575983[/C][C]0.333460952879915[/C][/ROW]
[ROW][C]30[/C][C]0.700354580536259[/C][C]0.599290838927482[/C][C]0.299645419463741[/C][/ROW]
[ROW][C]31[/C][C]0.732383532438455[/C][C]0.535232935123091[/C][C]0.267616467561545[/C][/ROW]
[ROW][C]32[/C][C]0.761695356833441[/C][C]0.476609286333118[/C][C]0.238304643166559[/C][/ROW]
[ROW][C]33[/C][C]0.786736917937634[/C][C]0.426526164124732[/C][C]0.213263082062366[/C][/ROW]
[ROW][C]34[/C][C]0.809164786410899[/C][C]0.381670427178202[/C][C]0.190835213589101[/C][/ROW]
[ROW][C]35[/C][C]0.82501441455207[/C][C]0.349971170895862[/C][C]0.174985585447931[/C][/ROW]
[ROW][C]36[/C][C]0.83683781179663[/C][C]0.32632437640674[/C][C]0.16316218820337[/C][/ROW]
[ROW][C]37[/C][C]0.849820926228696[/C][C]0.300358147542607[/C][C]0.150179073771304[/C][/ROW]
[ROW][C]38[/C][C]0.858127115715658[/C][C]0.283745768568684[/C][C]0.141872884284342[/C][/ROW]
[ROW][C]39[/C][C]0.849950668409654[/C][C]0.300098663180691[/C][C]0.150049331590346[/C][/ROW]
[ROW][C]40[/C][C]0.852864459559895[/C][C]0.29427108088021[/C][C]0.147135540440105[/C][/ROW]
[ROW][C]41[/C][C]0.859141491115126[/C][C]0.281717017769749[/C][C]0.140858508884874[/C][/ROW]
[ROW][C]42[/C][C]0.852139951008959[/C][C]0.295720097982082[/C][C]0.147860048991041[/C][/ROW]
[ROW][C]43[/C][C]0.835292630504561[/C][C]0.329414738990878[/C][C]0.164707369495439[/C][/ROW]
[ROW][C]44[/C][C]0.789913753846099[/C][C]0.420172492307802[/C][C]0.210086246153901[/C][/ROW]
[ROW][C]45[/C][C]0.738533116879262[/C][C]0.522933766241477[/C][C]0.261466883120738[/C][/ROW]
[ROW][C]46[/C][C]0.689700000686946[/C][C]0.620599998626107[/C][C]0.310299999313054[/C][/ROW]
[ROW][C]47[/C][C]0.612328247231261[/C][C]0.775343505537478[/C][C]0.387671752768739[/C][/ROW]
[ROW][C]48[/C][C]0.527239655565247[/C][C]0.945520688869507[/C][C]0.472760344434753[/C][/ROW]
[ROW][C]49[/C][C]0.496008307959512[/C][C]0.992016615919025[/C][C]0.503991692040488[/C][/ROW]
[ROW][C]50[/C][C]0.45152850079882[/C][C]0.90305700159764[/C][C]0.54847149920118[/C][/ROW]
[ROW][C]51[/C][C]0.413508365455171[/C][C]0.827016730910342[/C][C]0.586491634544829[/C][/ROW]
[ROW][C]52[/C][C]0.450320487177232[/C][C]0.900640974354464[/C][C]0.549679512822768[/C][/ROW]
[ROW][C]53[/C][C]0.545024455405107[/C][C]0.909951089189787[/C][C]0.454975544594893[/C][/ROW]
[ROW][C]54[/C][C]0.62758139581144[/C][C]0.74483720837712[/C][C]0.37241860418856[/C][/ROW]
[ROW][C]55[/C][C]0.582992188906159[/C][C]0.834015622187683[/C][C]0.417007811093841[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57966&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57966&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
50.0009605878267575660.001921175653515130.999039412173242
60.0001018646598754990.0002037293197509980.999898135340125
71.09268826022719e-052.18537652045439e-050.999989073117398
82.27519999069831e-064.55039998139663e-060.99999772480001
96.4677403364485e-061.2935480672897e-050.999993532259664
102.56278152715428e-055.12556305430856e-050.999974372184729
118.90142765236393e-050.0001780285530472790.999910985723476
120.0002892207540861410.0005784415081722830.999710779245914
130.0006964144289536680.001392828857907340.999303585571046
140.001973280306222310.003946560612444620.998026719693778
150.004087999348975840.008175998697951680.995912000651024
160.006669988168185660.01333997633637130.993330011831814
170.008866396392229720.01773279278445940.99113360360777
180.01257178590918670.02514357181837340.987428214090813
190.0197667490556240.0395334981112480.980233250944376
200.03390332365017120.06780664730034250.966096676349829
210.05580622592986410.1116124518597280.944193774070136
220.0854997641212590.1709995282425180.914500235878741
230.1283809696207080.2567619392414160.871619030379292
240.1958153953364830.3916307906729650.804184604663517
250.3011101459266060.6022202918532110.698889854073395
260.427729913881620.855459827763240.57227008611838
270.5458294808861350.908341038227730.454170519113865
280.6226776979300940.7546446041398120.377322302069906
290.6665390471200850.666921905759830.333460952879915
300.7003545805362590.5992908389274820.299645419463741
310.7323835324384550.5352329351230910.267616467561545
320.7616953568334410.4766092863331180.238304643166559
330.7867369179376340.4265261641247320.213263082062366
340.8091647864108990.3816704271782020.190835213589101
350.825014414552070.3499711708958620.174985585447931
360.836837811796630.326324376406740.16316218820337
370.8498209262286960.3003581475426070.150179073771304
380.8581271157156580.2837457685686840.141872884284342
390.8499506684096540.3000986631806910.150049331590346
400.8528644595598950.294271080880210.147135540440105
410.8591414911151260.2817170177697490.140858508884874
420.8521399510089590.2957200979820820.147860048991041
430.8352926305045610.3294147389908780.164707369495439
440.7899137538460990.4201724923078020.210086246153901
450.7385331168792620.5229337662414770.261466883120738
460.6897000006869460.6205999986261070.310299999313054
470.6123282472312610.7753435055374780.387671752768739
480.5272396555652470.9455206888695070.472760344434753
490.4960083079595120.9920166159190250.503991692040488
500.451528500798820.903057001597640.54847149920118
510.4135083654551710.8270167309103420.586491634544829
520.4503204871772320.9006409743544640.549679512822768
530.5450244554051070.9099510891897870.454975544594893
540.627581395811440.744837208377120.37241860418856
550.5829921889061590.8340156221876830.417007811093841







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level110.215686274509804NOK
5% type I error level150.294117647058824NOK
10% type I error level160.313725490196078NOK

\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 & 11 & 0.215686274509804 & NOK \tabularnewline
5% type I error level & 15 & 0.294117647058824 & NOK \tabularnewline
10% type I error level & 16 & 0.313725490196078 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57966&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]11[/C][C]0.215686274509804[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]15[/C][C]0.294117647058824[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]16[/C][C]0.313725490196078[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57966&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57966&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 level110.215686274509804NOK
5% type I error level150.294117647058824NOK
10% type I error level160.313725490196078NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$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')
}