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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 30 Nov 2010 12:59:21 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/30/t1291121853fhcok3pp03wvr5c.htm/, Retrieved Thu, 31 Oct 2024 22:59:20 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=103362, Retrieved Thu, 31 Oct 2024 22:59:20 +0000

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User-defined keywords

Estimated Impact

211

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 14:03:14] [b98453cac15ba1066b407e146608df68]
F    D    [Multiple Regression] [] [2010-11-26 13:22:51] [8a9a6f7c332640af31ddca253a8ded58]
F    D        [Multiple Regression] [ws 8] [2010-11-30 12:59:21] [86130087148d9c8eb48f66f03eaf10c2] [Current]

Feedback Forum

2010-12-05 10:33:51 [00c625c7d009d84797af914265b614f9] [reply] 
Lage adjusted R² waarde. De assumpties voor dit model zijn niet voldaan. 

Post a new message

Dataseries X:

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 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 & 8 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103362&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]8 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=103362&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103362&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation
vrijetijdsbesteding[t] = + 110.49 -1.72600000000005M1[t] -1.34400000000001M2[t] -1.28000000000001M3[t] -0.76400000000001M4[t] -0.722000000000008M5[t] -0.384000000000008M6[t] -0.216000000000008M7[t] + 0.165999999999991M8[t] + 1.49600000000000M9[t] + 1.69999999999999M10[t] -0.187500000000006M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
vrijetijdsbesteding[t] =  +  110.49 -1.72600000000005M1[t] -1.34400000000001M2[t] -1.28000000000001M3[t] -0.76400000000001M4[t] -0.722000000000008M5[t] -0.384000000000008M6[t] -0.216000000000008M7[t] +  0.165999999999991M8[t] +  1.49600000000000M9[t] +  1.69999999999999M10[t] -0.187500000000006M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103362&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]vrijetijdsbesteding[t] =  +  110.49 -1.72600000000005M1[t] -1.34400000000001M2[t] -1.28000000000001M3[t] -0.76400000000001M4[t] -0.722000000000008M5[t] -0.384000000000008M6[t] -0.216000000000008M7[t] +  0.165999999999991M8[t] +  1.49600000000000M9[t] +  1.69999999999999M10[t] -0.187500000000006M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103362&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103362&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
vrijetijdsbesteding[t] = + 110.49 -1.72600000000005M1[t] -1.34400000000001M2[t] -1.28000000000001M3[t] -0.76400000000001M4[t] -0.722000000000008M5[t] -0.384000000000008M6[t] -0.216000000000008M7[t] + 0.165999999999991M8[t] + 1.49600000000000M9[t] + 1.69999999999999M10[t] -0.187500000000006M11[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	110.49	3.049833	36.2282	0	0
M1	-1.72600000000005	4.09178	-0.4218	0.67512	0.33756
M2	-1.34400000000001	4.09178	-0.3285	0.744052	0.372026
M3	-1.28000000000001	4.09178	-0.3128	0.75583	0.377915
M4	-0.76400000000001	4.09178	-0.1867	0.852705	0.426352
M5	-0.722000000000008	4.09178	-0.1765	0.860714	0.430357
M6	-0.384000000000008	4.09178	-0.0938	0.925639	0.462819
M7	-0.216000000000008	4.09178	-0.0528	0.958129	0.479064
M8	0.165999999999991	4.09178	0.0406	0.967815	0.483907
M9	1.49600000000000	4.09178	0.3656	0.716331	0.358166
M10	1.69999999999999	4.09178	0.4155	0.679732	0.339866
M11	-0.187500000000006	4.313115	-0.0435	0.965513	0.482757

\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) & 110.49 & 3.049833 & 36.2282 & 0 & 0 \tabularnewline
M1 & -1.72600000000005 & 4.09178 & -0.4218 & 0.67512 & 0.33756 \tabularnewline
M2 & -1.34400000000001 & 4.09178 & -0.3285 & 0.744052 & 0.372026 \tabularnewline
M3 & -1.28000000000001 & 4.09178 & -0.3128 & 0.75583 & 0.377915 \tabularnewline
M4 & -0.76400000000001 & 4.09178 & -0.1867 & 0.852705 & 0.426352 \tabularnewline
M5 & -0.722000000000008 & 4.09178 & -0.1765 & 0.860714 & 0.430357 \tabularnewline
M6 & -0.384000000000008 & 4.09178 & -0.0938 & 0.925639 & 0.462819 \tabularnewline
M7 & -0.216000000000008 & 4.09178 & -0.0528 & 0.958129 & 0.479064 \tabularnewline
M8 & 0.165999999999991 & 4.09178 & 0.0406 & 0.967815 & 0.483907 \tabularnewline
M9 & 1.49600000000000 & 4.09178 & 0.3656 & 0.716331 & 0.358166 \tabularnewline
M10 & 1.69999999999999 & 4.09178 & 0.4155 & 0.679732 & 0.339866 \tabularnewline
M11 & -0.187500000000006 & 4.313115 & -0.0435 & 0.965513 & 0.482757 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103362&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]110.49[/C][C]3.049833[/C][C]36.2282[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-1.72600000000005[/C][C]4.09178[/C][C]-0.4218[/C][C]0.67512[/C][C]0.33756[/C][/ROW]
[ROW][C]M2[/C][C]-1.34400000000001[/C][C]4.09178[/C][C]-0.3285[/C][C]0.744052[/C][C]0.372026[/C][/ROW]
[ROW][C]M3[/C][C]-1.28000000000001[/C][C]4.09178[/C][C]-0.3128[/C][C]0.75583[/C][C]0.377915[/C][/ROW]
[ROW][C]M4[/C][C]-0.76400000000001[/C][C]4.09178[/C][C]-0.1867[/C][C]0.852705[/C][C]0.426352[/C][/ROW]
[ROW][C]M5[/C][C]-0.722000000000008[/C][C]4.09178[/C][C]-0.1765[/C][C]0.860714[/C][C]0.430357[/C][/ROW]
[ROW][C]M6[/C][C]-0.384000000000008[/C][C]4.09178[/C][C]-0.0938[/C][C]0.925639[/C][C]0.462819[/C][/ROW]
[ROW][C]M7[/C][C]-0.216000000000008[/C][C]4.09178[/C][C]-0.0528[/C][C]0.958129[/C][C]0.479064[/C][/ROW]
[ROW][C]M8[/C][C]0.165999999999991[/C][C]4.09178[/C][C]0.0406[/C][C]0.967815[/C][C]0.483907[/C][/ROW]
[ROW][C]M9[/C][C]1.49600000000000[/C][C]4.09178[/C][C]0.3656[/C][C]0.716331[/C][C]0.358166[/C][/ROW]
[ROW][C]M10[/C][C]1.69999999999999[/C][C]4.09178[/C][C]0.4155[/C][C]0.679732[/C][C]0.339866[/C][/ROW]
[ROW][C]M11[/C][C]-0.187500000000006[/C][C]4.313115[/C][C]-0.0435[/C][C]0.965513[/C][C]0.482757[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103362&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103362&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
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	110.49	3.049833	36.2282	0	0
M1	-1.72600000000005	4.09178	-0.4218	0.67512	0.33756
M2	-1.34400000000001	4.09178	-0.3285	0.744052	0.372026
M3	-1.28000000000001	4.09178	-0.3128	0.75583	0.377915
M4	-0.76400000000001	4.09178	-0.1867	0.852705	0.426352
M5	-0.722000000000008	4.09178	-0.1765	0.860714	0.430357
M6	-0.384000000000008	4.09178	-0.0938	0.925639	0.462819
M7	-0.216000000000008	4.09178	-0.0528	0.958129	0.479064
M8	0.165999999999991	4.09178	0.0406	0.967815	0.483907
M9	1.49600000000000	4.09178	0.3656	0.716331	0.358166
M10	1.69999999999999	4.09178	0.4155	0.679732	0.339866
M11	-0.187500000000006	4.313115	-0.0435	0.965513	0.482757

Multiple Linear Regression - Regression Statistics
Multiple R	0.184116543409472
R-squared	0.0338989015570519
Adjusted R-squared	-0.197125274157566
F-TEST (value)	0.146733134972536
F-TEST (DF numerator)	11
F-TEST (DF denominator)	46
p-value	0.999225903166059
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	6.09966569825223
Sum Squared Residuals	1711.47239500000

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.184116543409472 \tabularnewline
R-squared & 0.0338989015570519 \tabularnewline
Adjusted R-squared & -0.197125274157566 \tabularnewline
F-TEST (value) & 0.146733134972536 \tabularnewline
F-TEST (DF numerator) & 11 \tabularnewline
F-TEST (DF denominator) & 46 \tabularnewline
p-value & 0.999225903166059 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 6.09966569825223 \tabularnewline
Sum Squared Residuals & 1711.47239500000 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103362&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.184116543409472[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0338989015570519[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.197125274157566[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.146733134972536[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]11[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]46[/C][/ROW]
[ROW][C]p-value[/C][C]0.999225903166059[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]6.09966569825223[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1711.47239500000[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103362&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103362&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 R	0.184116543409472
R-squared	0.0338989015570519
Adjusted R-squared	-0.197125274157566
F-TEST (value)	0.146733134972536
F-TEST (DF numerator)	11
F-TEST (DF denominator)	46
p-value	0.999225903166059
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	6.09966569825223
Sum Squared Residuals	1711.47239500000

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	101.76	108.764000000000	-7.00400000000016
2	102.37	109.146	-6.77599999999999
3	102.38	109.21	-6.83000000000001
4	102.86	109.726	-6.866
5	102.87	109.768	-6.898
6	102.92	110.106	-7.186
7	102.95	110.274	-7.324
8	103.02	110.656	-7.636
9	104.08	111.986	-7.906
10	104.16	112.19	-8.03
11	104.24	110.3025	-6.06250000000001
12	104.33	110.49	-6.16000000000001
13	104.73	108.764	-4.03399999999995
14	104.86	109.146	-4.286
15	105.03	109.21	-4.18
16	105.62	109.726	-4.10599999999999
17	105.63	109.768	-4.13800000000001
18	105.63	110.106	-4.476
19	105.94	110.274	-4.334
20	106.61	110.656	-4.046
21	107.69	111.986	-4.29600000000000
22	107.78	112.19	-4.41
23	107.93	110.3025	-2.37250000000000
24	108.48	110.49	-2.01000000000000
25	108.14	108.764	-0.623999999999958
26	108.48	109.146	-0.665999999999992
27	108.48	109.21	-0.729999999999993
28	108.89	109.726	-0.835999999999997
29	108.93	109.768	-0.837999999999994
30	109.21	110.106	-0.896000000000004
31	109.47	110.274	-0.804000000000003
32	109.8	110.656	-0.856000000000001
33	111.73	111.986	-0.255999999999998
34	111.85	112.19	-0.340000000000002
35	112.12	110.3025	1.81750000000000
36	112.15	110.49	1.66000000000000
37	112.17	108.764	3.40600000000004
38	112.67	109.146	3.52400000000000
39	112.8	109.21	3.59
40	113.44	109.726	3.714
41	113.53	109.768	3.762
42	114.53	110.106	4.42400000000000
43	114.51	110.274	4.236
44	115.05	110.656	4.394
45	116.67	111.986	4.684
46	117.07	112.19	4.88
47	116.92	110.3025	6.6175
48	117	110.49	6.50999999999999
49	117.02	108.764	8.25600000000004
50	117.35	109.146	8.204
51	117.36	109.21	8.15
52	117.82	109.726	8.094
53	117.88	109.768	8.112
54	118.24	110.106	8.134
55	118.5	110.274	8.226
56	118.8	110.656	8.144
57	119.76	111.986	7.774
58	120.09	112.19	7.9

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 101.76 & 108.764000000000 & -7.00400000000016 \tabularnewline
2 & 102.37 & 109.146 & -6.77599999999999 \tabularnewline
3 & 102.38 & 109.21 & -6.83000000000001 \tabularnewline
4 & 102.86 & 109.726 & -6.866 \tabularnewline
5 & 102.87 & 109.768 & -6.898 \tabularnewline
6 & 102.92 & 110.106 & -7.186 \tabularnewline
7 & 102.95 & 110.274 & -7.324 \tabularnewline
8 & 103.02 & 110.656 & -7.636 \tabularnewline
9 & 104.08 & 111.986 & -7.906 \tabularnewline
10 & 104.16 & 112.19 & -8.03 \tabularnewline
11 & 104.24 & 110.3025 & -6.06250000000001 \tabularnewline
12 & 104.33 & 110.49 & -6.16000000000001 \tabularnewline
13 & 104.73 & 108.764 & -4.03399999999995 \tabularnewline
14 & 104.86 & 109.146 & -4.286 \tabularnewline
15 & 105.03 & 109.21 & -4.18 \tabularnewline
16 & 105.62 & 109.726 & -4.10599999999999 \tabularnewline
17 & 105.63 & 109.768 & -4.13800000000001 \tabularnewline
18 & 105.63 & 110.106 & -4.476 \tabularnewline
19 & 105.94 & 110.274 & -4.334 \tabularnewline
20 & 106.61 & 110.656 & -4.046 \tabularnewline
21 & 107.69 & 111.986 & -4.29600000000000 \tabularnewline
22 & 107.78 & 112.19 & -4.41 \tabularnewline
23 & 107.93 & 110.3025 & -2.37250000000000 \tabularnewline
24 & 108.48 & 110.49 & -2.01000000000000 \tabularnewline
25 & 108.14 & 108.764 & -0.623999999999958 \tabularnewline
26 & 108.48 & 109.146 & -0.665999999999992 \tabularnewline
27 & 108.48 & 109.21 & -0.729999999999993 \tabularnewline
28 & 108.89 & 109.726 & -0.835999999999997 \tabularnewline
29 & 108.93 & 109.768 & -0.837999999999994 \tabularnewline
30 & 109.21 & 110.106 & -0.896000000000004 \tabularnewline
31 & 109.47 & 110.274 & -0.804000000000003 \tabularnewline
32 & 109.8 & 110.656 & -0.856000000000001 \tabularnewline
33 & 111.73 & 111.986 & -0.255999999999998 \tabularnewline
34 & 111.85 & 112.19 & -0.340000000000002 \tabularnewline
35 & 112.12 & 110.3025 & 1.81750000000000 \tabularnewline
36 & 112.15 & 110.49 & 1.66000000000000 \tabularnewline
37 & 112.17 & 108.764 & 3.40600000000004 \tabularnewline
38 & 112.67 & 109.146 & 3.52400000000000 \tabularnewline
39 & 112.8 & 109.21 & 3.59 \tabularnewline
40 & 113.44 & 109.726 & 3.714 \tabularnewline
41 & 113.53 & 109.768 & 3.762 \tabularnewline
42 & 114.53 & 110.106 & 4.42400000000000 \tabularnewline
43 & 114.51 & 110.274 & 4.236 \tabularnewline
44 & 115.05 & 110.656 & 4.394 \tabularnewline
45 & 116.67 & 111.986 & 4.684 \tabularnewline
46 & 117.07 & 112.19 & 4.88 \tabularnewline
47 & 116.92 & 110.3025 & 6.6175 \tabularnewline
48 & 117 & 110.49 & 6.50999999999999 \tabularnewline
49 & 117.02 & 108.764 & 8.25600000000004 \tabularnewline
50 & 117.35 & 109.146 & 8.204 \tabularnewline
51 & 117.36 & 109.21 & 8.15 \tabularnewline
52 & 117.82 & 109.726 & 8.094 \tabularnewline
53 & 117.88 & 109.768 & 8.112 \tabularnewline
54 & 118.24 & 110.106 & 8.134 \tabularnewline
55 & 118.5 & 110.274 & 8.226 \tabularnewline
56 & 118.8 & 110.656 & 8.144 \tabularnewline
57 & 119.76 & 111.986 & 7.774 \tabularnewline
58 & 120.09 & 112.19 & 7.9 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103362&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]101.76[/C][C]108.764000000000[/C][C]-7.00400000000016[/C][/ROW]
[ROW][C]2[/C][C]102.37[/C][C]109.146[/C][C]-6.77599999999999[/C][/ROW]
[ROW][C]3[/C][C]102.38[/C][C]109.21[/C][C]-6.83000000000001[/C][/ROW]
[ROW][C]4[/C][C]102.86[/C][C]109.726[/C][C]-6.866[/C][/ROW]
[ROW][C]5[/C][C]102.87[/C][C]109.768[/C][C]-6.898[/C][/ROW]
[ROW][C]6[/C][C]102.92[/C][C]110.106[/C][C]-7.186[/C][/ROW]
[ROW][C]7[/C][C]102.95[/C][C]110.274[/C][C]-7.324[/C][/ROW]
[ROW][C]8[/C][C]103.02[/C][C]110.656[/C][C]-7.636[/C][/ROW]
[ROW][C]9[/C][C]104.08[/C][C]111.986[/C][C]-7.906[/C][/ROW]
[ROW][C]10[/C][C]104.16[/C][C]112.19[/C][C]-8.03[/C][/ROW]
[ROW][C]11[/C][C]104.24[/C][C]110.3025[/C][C]-6.06250000000001[/C][/ROW]
[ROW][C]12[/C][C]104.33[/C][C]110.49[/C][C]-6.16000000000001[/C][/ROW]
[ROW][C]13[/C][C]104.73[/C][C]108.764[/C][C]-4.03399999999995[/C][/ROW]
[ROW][C]14[/C][C]104.86[/C][C]109.146[/C][C]-4.286[/C][/ROW]
[ROW][C]15[/C][C]105.03[/C][C]109.21[/C][C]-4.18[/C][/ROW]
[ROW][C]16[/C][C]105.62[/C][C]109.726[/C][C]-4.10599999999999[/C][/ROW]
[ROW][C]17[/C][C]105.63[/C][C]109.768[/C][C]-4.13800000000001[/C][/ROW]
[ROW][C]18[/C][C]105.63[/C][C]110.106[/C][C]-4.476[/C][/ROW]
[ROW][C]19[/C][C]105.94[/C][C]110.274[/C][C]-4.334[/C][/ROW]
[ROW][C]20[/C][C]106.61[/C][C]110.656[/C][C]-4.046[/C][/ROW]
[ROW][C]21[/C][C]107.69[/C][C]111.986[/C][C]-4.29600000000000[/C][/ROW]
[ROW][C]22[/C][C]107.78[/C][C]112.19[/C][C]-4.41[/C][/ROW]
[ROW][C]23[/C][C]107.93[/C][C]110.3025[/C][C]-2.37250000000000[/C][/ROW]
[ROW][C]24[/C][C]108.48[/C][C]110.49[/C][C]-2.01000000000000[/C][/ROW]
[ROW][C]25[/C][C]108.14[/C][C]108.764[/C][C]-0.623999999999958[/C][/ROW]
[ROW][C]26[/C][C]108.48[/C][C]109.146[/C][C]-0.665999999999992[/C][/ROW]
[ROW][C]27[/C][C]108.48[/C][C]109.21[/C][C]-0.729999999999993[/C][/ROW]
[ROW][C]28[/C][C]108.89[/C][C]109.726[/C][C]-0.835999999999997[/C][/ROW]
[ROW][C]29[/C][C]108.93[/C][C]109.768[/C][C]-0.837999999999994[/C][/ROW]
[ROW][C]30[/C][C]109.21[/C][C]110.106[/C][C]-0.896000000000004[/C][/ROW]
[ROW][C]31[/C][C]109.47[/C][C]110.274[/C][C]-0.804000000000003[/C][/ROW]
[ROW][C]32[/C][C]109.8[/C][C]110.656[/C][C]-0.856000000000001[/C][/ROW]
[ROW][C]33[/C][C]111.73[/C][C]111.986[/C][C]-0.255999999999998[/C][/ROW]
[ROW][C]34[/C][C]111.85[/C][C]112.19[/C][C]-0.340000000000002[/C][/ROW]
[ROW][C]35[/C][C]112.12[/C][C]110.3025[/C][C]1.81750000000000[/C][/ROW]
[ROW][C]36[/C][C]112.15[/C][C]110.49[/C][C]1.66000000000000[/C][/ROW]
[ROW][C]37[/C][C]112.17[/C][C]108.764[/C][C]3.40600000000004[/C][/ROW]
[ROW][C]38[/C][C]112.67[/C][C]109.146[/C][C]3.52400000000000[/C][/ROW]
[ROW][C]39[/C][C]112.8[/C][C]109.21[/C][C]3.59[/C][/ROW]
[ROW][C]40[/C][C]113.44[/C][C]109.726[/C][C]3.714[/C][/ROW]
[ROW][C]41[/C][C]113.53[/C][C]109.768[/C][C]3.762[/C][/ROW]
[ROW][C]42[/C][C]114.53[/C][C]110.106[/C][C]4.42400000000000[/C][/ROW]
[ROW][C]43[/C][C]114.51[/C][C]110.274[/C][C]4.236[/C][/ROW]
[ROW][C]44[/C][C]115.05[/C][C]110.656[/C][C]4.394[/C][/ROW]
[ROW][C]45[/C][C]116.67[/C][C]111.986[/C][C]4.684[/C][/ROW]
[ROW][C]46[/C][C]117.07[/C][C]112.19[/C][C]4.88[/C][/ROW]
[ROW][C]47[/C][C]116.92[/C][C]110.3025[/C][C]6.6175[/C][/ROW]
[ROW][C]48[/C][C]117[/C][C]110.49[/C][C]6.50999999999999[/C][/ROW]
[ROW][C]49[/C][C]117.02[/C][C]108.764[/C][C]8.25600000000004[/C][/ROW]
[ROW][C]50[/C][C]117.35[/C][C]109.146[/C][C]8.204[/C][/ROW]
[ROW][C]51[/C][C]117.36[/C][C]109.21[/C][C]8.15[/C][/ROW]
[ROW][C]52[/C][C]117.82[/C][C]109.726[/C][C]8.094[/C][/ROW]
[ROW][C]53[/C][C]117.88[/C][C]109.768[/C][C]8.112[/C][/ROW]
[ROW][C]54[/C][C]118.24[/C][C]110.106[/C][C]8.134[/C][/ROW]
[ROW][C]55[/C][C]118.5[/C][C]110.274[/C][C]8.226[/C][/ROW]
[ROW][C]56[/C][C]118.8[/C][C]110.656[/C][C]8.144[/C][/ROW]
[ROW][C]57[/C][C]119.76[/C][C]111.986[/C][C]7.774[/C][/ROW]
[ROW][C]58[/C][C]120.09[/C][C]112.19[/C][C]7.9[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103362&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103362&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 Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	101.76	108.764000000000	-7.00400000000016
2	102.37	109.146	-6.77599999999999
3	102.38	109.21	-6.83000000000001
4	102.86	109.726	-6.866
5	102.87	109.768	-6.898
6	102.92	110.106	-7.186
7	102.95	110.274	-7.324
8	103.02	110.656	-7.636
9	104.08	111.986	-7.906
10	104.16	112.19	-8.03
11	104.24	110.3025	-6.06250000000001
12	104.33	110.49	-6.16000000000001
13	104.73	108.764	-4.03399999999995
14	104.86	109.146	-4.286
15	105.03	109.21	-4.18
16	105.62	109.726	-4.10599999999999
17	105.63	109.768	-4.13800000000001
18	105.63	110.106	-4.476
19	105.94	110.274	-4.334
20	106.61	110.656	-4.046
21	107.69	111.986	-4.29600000000000
22	107.78	112.19	-4.41
23	107.93	110.3025	-2.37250000000000
24	108.48	110.49	-2.01000000000000
25	108.14	108.764	-0.623999999999958
26	108.48	109.146	-0.665999999999992
27	108.48	109.21	-0.729999999999993
28	108.89	109.726	-0.835999999999997
29	108.93	109.768	-0.837999999999994
30	109.21	110.106	-0.896000000000004
31	109.47	110.274	-0.804000000000003
32	109.8	110.656	-0.856000000000001
33	111.73	111.986	-0.255999999999998
34	111.85	112.19	-0.340000000000002
35	112.12	110.3025	1.81750000000000
36	112.15	110.49	1.66000000000000
37	112.17	108.764	3.40600000000004
38	112.67	109.146	3.52400000000000
39	112.8	109.21	3.59
40	113.44	109.726	3.714
41	113.53	109.768	3.762
42	114.53	110.106	4.42400000000000
43	114.51	110.274	4.236
44	115.05	110.656	4.394
45	116.67	111.986	4.684
46	117.07	112.19	4.88
47	116.92	110.3025	6.6175
48	117	110.49	6.50999999999999
49	117.02	108.764	8.25600000000004
50	117.35	109.146	8.204
51	117.36	109.21	8.15
52	117.82	109.726	8.094
53	117.88	109.768	8.112
54	118.24	110.106	8.134
55	118.5	110.274	8.226
56	118.8	110.656	8.144
57	119.76	111.986	7.774
58	120.09	112.19	7.9

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
15	0.0645893929315995	0.129178785863199	0.9354106070684
16	0.0359893264042393	0.0719786528084787	0.96401067359576
17	0.0213879206131229	0.0427758412262459	0.978612079386877
18	0.0136170394310909	0.0272340788621817	0.98638296056891
19	0.0101066555341159	0.0202133110682319	0.989893344465884
20	0.00963159584388155	0.0192631916877631	0.990368404156118
21	0.00996493520835916	0.0199298704167183	0.99003506479164
22	0.0112566868735290	0.0225133737470579	0.98874331312647
23	0.0110864841604224	0.0221729683208449	0.988913515839578
24	0.0121488516846593	0.0242977033693186	0.98785114831534
25	0.0209377049293228	0.0418754098586457	0.979062295070677
26	0.0325552412018408	0.0651104824036817	0.96744475879816
27	0.0471685673852682	0.0943371347705364	0.952831432614732
28	0.0659306624002647	0.131861324800529	0.934069337599735
29	0.0923365843578951	0.184673168715790	0.907663415642105
30	0.140624188991067	0.281248377982135	0.859375811008933
31	0.210200834755615	0.42040166951123	0.789799165244385
32	0.312124005241855	0.62424801048371	0.687875994758145
33	0.444300268001602	0.888600536003204	0.555699731998398
34	0.613391200454309	0.773217599091382	0.386608799545691
35	0.66289280374485	0.674214392510299	0.337107196255150
36	0.701594229772474	0.596811540455053	0.298405770227526
37	0.766452204323642	0.467095591352717	0.233547795676358
38	0.810691125505356	0.378617748989288	0.189308874494644
39	0.839769048191784	0.320461903616432	0.160230951808216
40	0.856206312719432	0.287587374561137	0.143793687280568
41	0.86615000140447	0.267699997191059	0.133849998595530
42	0.853261402125933	0.293477195748133	0.146738597874067
43	0.838073030879997	0.323853938240006	0.161926969120003

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 & 0.0645893929315995 & 0.129178785863199 & 0.9354106070684 \tabularnewline
16 & 0.0359893264042393 & 0.0719786528084787 & 0.96401067359576 \tabularnewline
17 & 0.0213879206131229 & 0.0427758412262459 & 0.978612079386877 \tabularnewline
18 & 0.0136170394310909 & 0.0272340788621817 & 0.98638296056891 \tabularnewline
19 & 0.0101066555341159 & 0.0202133110682319 & 0.989893344465884 \tabularnewline
20 & 0.00963159584388155 & 0.0192631916877631 & 0.990368404156118 \tabularnewline
21 & 0.00996493520835916 & 0.0199298704167183 & 0.99003506479164 \tabularnewline
22 & 0.0112566868735290 & 0.0225133737470579 & 0.98874331312647 \tabularnewline
23 & 0.0110864841604224 & 0.0221729683208449 & 0.988913515839578 \tabularnewline
24 & 0.0121488516846593 & 0.0242977033693186 & 0.98785114831534 \tabularnewline
25 & 0.0209377049293228 & 0.0418754098586457 & 0.979062295070677 \tabularnewline
26 & 0.0325552412018408 & 0.0651104824036817 & 0.96744475879816 \tabularnewline
27 & 0.0471685673852682 & 0.0943371347705364 & 0.952831432614732 \tabularnewline
28 & 0.0659306624002647 & 0.131861324800529 & 0.934069337599735 \tabularnewline
29 & 0.0923365843578951 & 0.184673168715790 & 0.907663415642105 \tabularnewline
30 & 0.140624188991067 & 0.281248377982135 & 0.859375811008933 \tabularnewline
31 & 0.210200834755615 & 0.42040166951123 & 0.789799165244385 \tabularnewline
32 & 0.312124005241855 & 0.62424801048371 & 0.687875994758145 \tabularnewline
33 & 0.444300268001602 & 0.888600536003204 & 0.555699731998398 \tabularnewline
34 & 0.613391200454309 & 0.773217599091382 & 0.386608799545691 \tabularnewline
35 & 0.66289280374485 & 0.674214392510299 & 0.337107196255150 \tabularnewline
36 & 0.701594229772474 & 0.596811540455053 & 0.298405770227526 \tabularnewline
37 & 0.766452204323642 & 0.467095591352717 & 0.233547795676358 \tabularnewline
38 & 0.810691125505356 & 0.378617748989288 & 0.189308874494644 \tabularnewline
39 & 0.839769048191784 & 0.320461903616432 & 0.160230951808216 \tabularnewline
40 & 0.856206312719432 & 0.287587374561137 & 0.143793687280568 \tabularnewline
41 & 0.86615000140447 & 0.267699997191059 & 0.133849998595530 \tabularnewline
42 & 0.853261402125933 & 0.293477195748133 & 0.146738597874067 \tabularnewline
43 & 0.838073030879997 & 0.323853938240006 & 0.161926969120003 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103362&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]15[/C][C]0.0645893929315995[/C][C]0.129178785863199[/C][C]0.9354106070684[/C][/ROW]
[ROW][C]16[/C][C]0.0359893264042393[/C][C]0.0719786528084787[/C][C]0.96401067359576[/C][/ROW]
[ROW][C]17[/C][C]0.0213879206131229[/C][C]0.0427758412262459[/C][C]0.978612079386877[/C][/ROW]
[ROW][C]18[/C][C]0.0136170394310909[/C][C]0.0272340788621817[/C][C]0.98638296056891[/C][/ROW]
[ROW][C]19[/C][C]0.0101066555341159[/C][C]0.0202133110682319[/C][C]0.989893344465884[/C][/ROW]
[ROW][C]20[/C][C]0.00963159584388155[/C][C]0.0192631916877631[/C][C]0.990368404156118[/C][/ROW]
[ROW][C]21[/C][C]0.00996493520835916[/C][C]0.0199298704167183[/C][C]0.99003506479164[/C][/ROW]
[ROW][C]22[/C][C]0.0112566868735290[/C][C]0.0225133737470579[/C][C]0.98874331312647[/C][/ROW]
[ROW][C]23[/C][C]0.0110864841604224[/C][C]0.0221729683208449[/C][C]0.988913515839578[/C][/ROW]
[ROW][C]24[/C][C]0.0121488516846593[/C][C]0.0242977033693186[/C][C]0.98785114831534[/C][/ROW]
[ROW][C]25[/C][C]0.0209377049293228[/C][C]0.0418754098586457[/C][C]0.979062295070677[/C][/ROW]
[ROW][C]26[/C][C]0.0325552412018408[/C][C]0.0651104824036817[/C][C]0.96744475879816[/C][/ROW]
[ROW][C]27[/C][C]0.0471685673852682[/C][C]0.0943371347705364[/C][C]0.952831432614732[/C][/ROW]
[ROW][C]28[/C][C]0.0659306624002647[/C][C]0.131861324800529[/C][C]0.934069337599735[/C][/ROW]
[ROW][C]29[/C][C]0.0923365843578951[/C][C]0.184673168715790[/C][C]0.907663415642105[/C][/ROW]
[ROW][C]30[/C][C]0.140624188991067[/C][C]0.281248377982135[/C][C]0.859375811008933[/C][/ROW]
[ROW][C]31[/C][C]0.210200834755615[/C][C]0.42040166951123[/C][C]0.789799165244385[/C][/ROW]
[ROW][C]32[/C][C]0.312124005241855[/C][C]0.62424801048371[/C][C]0.687875994758145[/C][/ROW]
[ROW][C]33[/C][C]0.444300268001602[/C][C]0.888600536003204[/C][C]0.555699731998398[/C][/ROW]
[ROW][C]34[/C][C]0.613391200454309[/C][C]0.773217599091382[/C][C]0.386608799545691[/C][/ROW]
[ROW][C]35[/C][C]0.66289280374485[/C][C]0.674214392510299[/C][C]0.337107196255150[/C][/ROW]
[ROW][C]36[/C][C]0.701594229772474[/C][C]0.596811540455053[/C][C]0.298405770227526[/C][/ROW]
[ROW][C]37[/C][C]0.766452204323642[/C][C]0.467095591352717[/C][C]0.233547795676358[/C][/ROW]
[ROW][C]38[/C][C]0.810691125505356[/C][C]0.378617748989288[/C][C]0.189308874494644[/C][/ROW]
[ROW][C]39[/C][C]0.839769048191784[/C][C]0.320461903616432[/C][C]0.160230951808216[/C][/ROW]
[ROW][C]40[/C][C]0.856206312719432[/C][C]0.287587374561137[/C][C]0.143793687280568[/C][/ROW]
[ROW][C]41[/C][C]0.86615000140447[/C][C]0.267699997191059[/C][C]0.133849998595530[/C][/ROW]
[ROW][C]42[/C][C]0.853261402125933[/C][C]0.293477195748133[/C][C]0.146738597874067[/C][/ROW]
[ROW][C]43[/C][C]0.838073030879997[/C][C]0.323853938240006[/C][C]0.161926969120003[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103362&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103362&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-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
15	0.0645893929315995	0.129178785863199	0.9354106070684
16	0.0359893264042393	0.0719786528084787	0.96401067359576
17	0.0213879206131229	0.0427758412262459	0.978612079386877
18	0.0136170394310909	0.0272340788621817	0.98638296056891
19	0.0101066555341159	0.0202133110682319	0.989893344465884
20	0.00963159584388155	0.0192631916877631	0.990368404156118
21	0.00996493520835916	0.0199298704167183	0.99003506479164
22	0.0112566868735290	0.0225133737470579	0.98874331312647
23	0.0110864841604224	0.0221729683208449	0.988913515839578
24	0.0121488516846593	0.0242977033693186	0.98785114831534
25	0.0209377049293228	0.0418754098586457	0.979062295070677
26	0.0325552412018408	0.0651104824036817	0.96744475879816
27	0.0471685673852682	0.0943371347705364	0.952831432614732
28	0.0659306624002647	0.131861324800529	0.934069337599735
29	0.0923365843578951	0.184673168715790	0.907663415642105
30	0.140624188991067	0.281248377982135	0.859375811008933
31	0.210200834755615	0.42040166951123	0.789799165244385
32	0.312124005241855	0.62424801048371	0.687875994758145
33	0.444300268001602	0.888600536003204	0.555699731998398
34	0.613391200454309	0.773217599091382	0.386608799545691
35	0.66289280374485	0.674214392510299	0.337107196255150
36	0.701594229772474	0.596811540455053	0.298405770227526
37	0.766452204323642	0.467095591352717	0.233547795676358
38	0.810691125505356	0.378617748989288	0.189308874494644
39	0.839769048191784	0.320461903616432	0.160230951808216
40	0.856206312719432	0.287587374561137	0.143793687280568
41	0.86615000140447	0.267699997191059	0.133849998595530
42	0.853261402125933	0.293477195748133	0.146738597874067
43	0.838073030879997	0.323853938240006	0.161926969120003

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	9	0.310344827586207	NOK
10% type I error level	12	0.413793103448276	NOK

\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 & 9 & 0.310344827586207 & NOK \tabularnewline
10% type I error level & 12 & 0.413793103448276 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103362&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]9[/C][C]0.310344827586207[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]12[/C][C]0.413793103448276[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103362&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103362&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 tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	9	0.310344827586207	NOK
10% type I error level	12	0.413793103448276	NOK

Figure 1

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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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code