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rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 18 Nov 2009 09:28:13 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/18/t1258561844i4cnlkdwzybvjze.htm/, Retrieved Sun, 05 May 2024 18:45:43 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57517, Retrieved Sun, 05 May 2024 18:45:43 +0000

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Estimated Impact

145

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-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-   PD      [Multiple Regression] [Multiple regressi...] [2009-11-18 16:28:13] [fe2edc5b0acc9545190e03904e9be55e] [Current]

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2.97	101.1	2.98	3.01	3.06	3.12	3.58
3.02	100.93	2.97	2.98	3.01	3.06	3.52
3.07	100.85	3.02	2.97	2.98	3.01	3.45
3.18	100.93	3.07	3.02	2.97	2.98	3.36
3.29	99.6	3.18	3.07	3.02	2.97	3.27
3.43	101.88	3.29	3.18	3.07	3.02	3.21
3.61	101.81	3.43	3.29	3.18	3.07	3.19
3.74	102.38	3.61	3.43	3.29	3.18	3.16
3.87	102.74	3.74	3.61	3.43	3.29	3.12
3.88	102.82	3.87	3.74	3.61	3.43	3.06
4.09	101.72	3.88	3.87	3.74	3.61	3.01
4.19	103.47	4.09	3.88	3.87	3.74	2.98
4.2	102.98	4.19	4.09	3.88	3.87	2.97
4.29	102.68	4.2	4.19	4.09	3.88	3.02
4.37	102.9	4.29	4.2	4.19	4.09	3.07
4.47	103.03	4.37	4.29	4.2	4.19	3.18
4.61	101.29	4.47	4.37	4.29	4.2	3.29
4.65	103.69	4.61	4.47	4.37	4.29	3.43
4.69	103.68	4.65	4.61	4.47	4.37	3.61
4.82	104.2	4.69	4.65	4.61	4.47	3.74
4.86	104.08	4.82	4.69	4.65	4.61	3.87
4.87	104.16	4.86	4.82	4.69	4.65	3.88
5.01	103.05	4.87	4.86	4.82	4.69	4.09
5.03	104.66	5.01	4.87	4.86	4.82	4.19
5.13	104.46	5.03	5.01	4.87	4.86	4.2
5.18	104.95	5.13	5.03	5.01	4.87	4.29
5.21	105.85	5.18	5.13	5.03	5.01	4.37
5.26	106.23	5.21	5.18	5.13	5.03	4.47
5.25	104.86	5.26	5.21	5.18	5.13	4.61
5.2	107.44	5.25	5.26	5.21	5.18	4.65
5.16	108.23	5.2	5.25	5.26	5.21	4.69
5.19	108.45	5.16	5.2	5.25	5.26	4.82
5.39	109.39	5.19	5.16	5.2	5.25	4.86
5.58	110.15	5.39	5.19	5.16	5.2	4.87
5.76	109.13	5.58	5.39	5.19	5.16	5.01
5.89	110.28	5.76	5.58	5.39	5.19	5.03
5.98	110.17	5.89	5.76	5.58	5.39	5.13
6.02	109.99	5.98	5.89	5.76	5.58	5.18
5.62	109.26	6.02	5.98	5.89	5.76	5.21
4.87	109.11	5.62	6.02	5.98	5.89	5.26
4.24	107.06	4.87	5.62	6.02	5.98	5.25
4.02	109.53	4.24	4.87	5.62	6.02	5.2
3.74	108.92	4.02	4.24	4.87	5.62	5.16
3.45	109.24	3.74	4.02	4.24	4.87	5.19
3.34	109.12	3.45	3.74	4.02	4.24	5.39
3.21	109	3.34	3.45	3.74	4.02	5.58
3.12	107.23	3.21	3.34	3.45	3.74	5.76
3.04	109.49	3.12	3.21	3.34	3.45	5.89

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57517&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57517&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57517&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	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Y[t] = -1.39732048460966 + 0.0161718963298520X[t] + 2.00157312691411y1[t] -1.56424107271801y2[t] + 0.790011335492396y3[t] -0.262256734663169y4[t] -0.0424262947310977y12[t] + 0.113985026448485M1[t] + 0.0815647206202224M2[t] -0.0294406678719151M3[t] + 0.0370825333025232M4[t] + 0.0878443689176442M5[t] + 0.0948251430698515M6[t] + 0.0467429337123727M7[t] + 0.104716315344380M8[t] + 0.124092289711188M9[t] + 0.0282583110835392M10[t] + 0.220714286663328M11[t] -0.00206216097793336t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  -1.39732048460966 +  0.0161718963298520X[t] +  2.00157312691411y1[t] -1.56424107271801y2[t] +  0.790011335492396y3[t] -0.262256734663169y4[t] -0.0424262947310977y12[t] +  0.113985026448485M1[t] +  0.0815647206202224M2[t] -0.0294406678719151M3[t] +  0.0370825333025232M4[t] +  0.0878443689176442M5[t] +  0.0948251430698515M6[t] +  0.0467429337123727M7[t] +  0.104716315344380M8[t] +  0.124092289711188M9[t] +  0.0282583110835392M10[t] +  0.220714286663328M11[t] -0.00206216097793336t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57517&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  -1.39732048460966 +  0.0161718963298520X[t] +  2.00157312691411y1[t] -1.56424107271801y2[t] +  0.790011335492396y3[t] -0.262256734663169y4[t] -0.0424262947310977y12[t] +  0.113985026448485M1[t] +  0.0815647206202224M2[t] -0.0294406678719151M3[t] +  0.0370825333025232M4[t] +  0.0878443689176442M5[t] +  0.0948251430698515M6[t] +  0.0467429337123727M7[t] +  0.104716315344380M8[t] +  0.124092289711188M9[t] +  0.0282583110835392M10[t] +  0.220714286663328M11[t] -0.00206216097793336t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57517&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57517&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] = -1.39732048460966 + 0.0161718963298520X[t] + 2.00157312691411y1[t] -1.56424107271801y2[t] + 0.790011335492396y3[t] -0.262256734663169y4[t] -0.0424262947310977y12[t] + 0.113985026448485M1[t] + 0.0815647206202224M2[t] -0.0294406678719151M3[t] + 0.0370825333025232M4[t] + 0.0878443689176442M5[t] + 0.0948251430698515M6[t] + 0.0467429337123727M7[t] + 0.104716315344380M8[t] + 0.124092289711188M9[t] + 0.0282583110835392M10[t] + 0.220714286663328M11[t] -0.00206216097793336t + 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)	-1.39732048460966	2.096764	-0.6664	0.510411	0.255206
X	0.0161718963298520	0.02228	0.7258	0.473752	0.236876
y1	2.00157312691411	0.184845	10.8284	0	0
y2	-1.56424107271801	0.396628	-3.9439	0.000466	0.000233
y3	0.790011335492396	0.402015	1.9651	0.059043	0.029522
y4	-0.262256734663169	0.201902	-1.2989	0.204201	0.102101
y12	-0.0424262947310977	0.067502	-0.6285	0.534581	0.267291
M1	0.113985026448485	0.097171	1.173	0.250325	0.125162
M2	0.0815647206202224	0.088295	0.9238	0.363229	0.181614
M3	-0.0294406678719151	0.090153	-0.3266	0.746342	0.373171
M4	0.0370825333025232	0.098302	0.3772	0.708748	0.354374
M5	0.0878443689176442	0.105279	0.8344	0.41088	0.20544
M6	0.0948251430698515	0.092153	1.029	0.311986	0.155993
M7	0.0467429337123727	0.091547	0.5106	0.613502	0.306751
M8	0.104716315344380	0.093043	1.1255	0.269622	0.134811
M9	0.124092289711188	0.085637	1.4491	0.15805	0.079025
M10	0.0282583110835392	0.083982	0.3365	0.738931	0.369466
M11	0.220714286663328	0.093076	2.3713	0.024582	0.012291
t	-0.00206216097793336	0.005175	-0.3985	0.693167	0.346583

\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) & -1.39732048460966 & 2.096764 & -0.6664 & 0.510411 & 0.255206 \tabularnewline
X & 0.0161718963298520 & 0.02228 & 0.7258 & 0.473752 & 0.236876 \tabularnewline
y1 & 2.00157312691411 & 0.184845 & 10.8284 & 0 & 0 \tabularnewline
y2 & -1.56424107271801 & 0.396628 & -3.9439 & 0.000466 & 0.000233 \tabularnewline
y3 & 0.790011335492396 & 0.402015 & 1.9651 & 0.059043 & 0.029522 \tabularnewline
y4 & -0.262256734663169 & 0.201902 & -1.2989 & 0.204201 & 0.102101 \tabularnewline
y12 & -0.0424262947310977 & 0.067502 & -0.6285 & 0.534581 & 0.267291 \tabularnewline
M1 & 0.113985026448485 & 0.097171 & 1.173 & 0.250325 & 0.125162 \tabularnewline
M2 & 0.0815647206202224 & 0.088295 & 0.9238 & 0.363229 & 0.181614 \tabularnewline
M3 & -0.0294406678719151 & 0.090153 & -0.3266 & 0.746342 & 0.373171 \tabularnewline
M4 & 0.0370825333025232 & 0.098302 & 0.3772 & 0.708748 & 0.354374 \tabularnewline
M5 & 0.0878443689176442 & 0.105279 & 0.8344 & 0.41088 & 0.20544 \tabularnewline
M6 & 0.0948251430698515 & 0.092153 & 1.029 & 0.311986 & 0.155993 \tabularnewline
M7 & 0.0467429337123727 & 0.091547 & 0.5106 & 0.613502 & 0.306751 \tabularnewline
M8 & 0.104716315344380 & 0.093043 & 1.1255 & 0.269622 & 0.134811 \tabularnewline
M9 & 0.124092289711188 & 0.085637 & 1.4491 & 0.15805 & 0.079025 \tabularnewline
M10 & 0.0282583110835392 & 0.083982 & 0.3365 & 0.738931 & 0.369466 \tabularnewline
M11 & 0.220714286663328 & 0.093076 & 2.3713 & 0.024582 & 0.012291 \tabularnewline
t & -0.00206216097793336 & 0.005175 & -0.3985 & 0.693167 & 0.346583 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57517&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]-1.39732048460966[/C][C]2.096764[/C][C]-0.6664[/C][C]0.510411[/C][C]0.255206[/C][/ROW]
[ROW][C]X[/C][C]0.0161718963298520[/C][C]0.02228[/C][C]0.7258[/C][C]0.473752[/C][C]0.236876[/C][/ROW]
[ROW][C]y1[/C][C]2.00157312691411[/C][C]0.184845[/C][C]10.8284[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]y2[/C][C]-1.56424107271801[/C][C]0.396628[/C][C]-3.9439[/C][C]0.000466[/C][C]0.000233[/C][/ROW]
[ROW][C]y3[/C][C]0.790011335492396[/C][C]0.402015[/C][C]1.9651[/C][C]0.059043[/C][C]0.029522[/C][/ROW]
[ROW][C]y4[/C][C]-0.262256734663169[/C][C]0.201902[/C][C]-1.2989[/C][C]0.204201[/C][C]0.102101[/C][/ROW]
[ROW][C]y12[/C][C]-0.0424262947310977[/C][C]0.067502[/C][C]-0.6285[/C][C]0.534581[/C][C]0.267291[/C][/ROW]
[ROW][C]M1[/C][C]0.113985026448485[/C][C]0.097171[/C][C]1.173[/C][C]0.250325[/C][C]0.125162[/C][/ROW]
[ROW][C]M2[/C][C]0.0815647206202224[/C][C]0.088295[/C][C]0.9238[/C][C]0.363229[/C][C]0.181614[/C][/ROW]
[ROW][C]M3[/C][C]-0.0294406678719151[/C][C]0.090153[/C][C]-0.3266[/C][C]0.746342[/C][C]0.373171[/C][/ROW]
[ROW][C]M4[/C][C]0.0370825333025232[/C][C]0.098302[/C][C]0.3772[/C][C]0.708748[/C][C]0.354374[/C][/ROW]
[ROW][C]M5[/C][C]0.0878443689176442[/C][C]0.105279[/C][C]0.8344[/C][C]0.41088[/C][C]0.20544[/C][/ROW]
[ROW][C]M6[/C][C]0.0948251430698515[/C][C]0.092153[/C][C]1.029[/C][C]0.311986[/C][C]0.155993[/C][/ROW]
[ROW][C]M7[/C][C]0.0467429337123727[/C][C]0.091547[/C][C]0.5106[/C][C]0.613502[/C][C]0.306751[/C][/ROW]
[ROW][C]M8[/C][C]0.104716315344380[/C][C]0.093043[/C][C]1.1255[/C][C]0.269622[/C][C]0.134811[/C][/ROW]
[ROW][C]M9[/C][C]0.124092289711188[/C][C]0.085637[/C][C]1.4491[/C][C]0.15805[/C][C]0.079025[/C][/ROW]
[ROW][C]M10[/C][C]0.0282583110835392[/C][C]0.083982[/C][C]0.3365[/C][C]0.738931[/C][C]0.369466[/C][/ROW]
[ROW][C]M11[/C][C]0.220714286663328[/C][C]0.093076[/C][C]2.3713[/C][C]0.024582[/C][C]0.012291[/C][/ROW]
[ROW][C]t[/C][C]-0.00206216097793336[/C][C]0.005175[/C][C]-0.3985[/C][C]0.693167[/C][C]0.346583[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57517&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57517&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)	-1.39732048460966	2.096764	-0.6664	0.510411	0.255206
X	0.0161718963298520	0.02228	0.7258	0.473752	0.236876
y1	2.00157312691411	0.184845	10.8284	0	0
y2	-1.56424107271801	0.396628	-3.9439	0.000466	0.000233
y3	0.790011335492396	0.402015	1.9651	0.059043	0.029522
y4	-0.262256734663169	0.201902	-1.2989	0.204201	0.102101
y12	-0.0424262947310977	0.067502	-0.6285	0.534581	0.267291
M1	0.113985026448485	0.097171	1.173	0.250325	0.125162
M2	0.0815647206202224	0.088295	0.9238	0.363229	0.181614
M3	-0.0294406678719151	0.090153	-0.3266	0.746342	0.373171
M4	0.0370825333025232	0.098302	0.3772	0.708748	0.354374
M5	0.0878443689176442	0.105279	0.8344	0.41088	0.20544
M6	0.0948251430698515	0.092153	1.029	0.311986	0.155993
M7	0.0467429337123727	0.091547	0.5106	0.613502	0.306751
M8	0.104716315344380	0.093043	1.1255	0.269622	0.134811
M9	0.124092289711188	0.085637	1.4491	0.15805	0.079025
M10	0.0282583110835392	0.083982	0.3365	0.738931	0.369466
M11	0.220714286663328	0.093076	2.3713	0.024582	0.012291
t	-0.00206216097793336	0.005175	-0.3985	0.693167	0.346583

Multiple Linear Regression - Regression Statistics
Multiple R	0.995022541782589
R-squared	0.990069858655483
Adjusted R-squared	0.983906322648542
F-TEST (value)	160.6334184696
F-TEST (DF numerator)	18
F-TEST (DF denominator)	29
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.114958626496740
Sum Squared Residuals	0.38324908837449

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.995022541782589 \tabularnewline
R-squared & 0.990069858655483 \tabularnewline
Adjusted R-squared & 0.983906322648542 \tabularnewline
F-TEST (value) & 160.6334184696 \tabularnewline
F-TEST (DF numerator) & 18 \tabularnewline
F-TEST (DF denominator) & 29 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.114958626496740 \tabularnewline
Sum Squared Residuals & 0.38324908837449 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57517&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.995022541782589[/C][/ROW]
[ROW][C]R-squared[/C][C]0.990069858655483[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.983906322648542[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]160.6334184696[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]18[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]29[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.114958626496740[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.38324908837449[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57517&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57517&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	0.995022541782589
R-squared	0.990069858655483
Adjusted R-squared	0.983906322648542
F-TEST (value)	160.6334184696
F-TEST (DF numerator)	18
F-TEST (DF denominator)	29
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.114958626496740
Sum Squared Residuals	0.38324908837449

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	2.97	3.05321092845207	-0.0832109284520703
2	3.02	3.02167115517124	-0.00167115517124363
3	3.07	3.01541325836723	0.0545867416327652
4	3.18	3.1068206081907	0.0731793918092982
5	3.29	3.32191415168089	-0.0319141516808896
6	3.43	3.44074452217412	-0.0107445221741233
7	3.61	3.57225877493024	0.0377412250697556
8	3.74	3.8380031840895	-0.0980031840894992
9	3.87	3.92323039151194	-0.0532303915119448
10	3.88	3.9915128458779	-0.111512845877897
11	4.09	4.03839854244391	0.0516014575560893
12	4.19	4.31849174625441	-0.128491746254406
13	4.2	4.26838807073998	-0.0683880707399832
14	4.29	4.25380415740239	0.0361958425976128
15	4.37	4.33059950035339	0.0394004996466066
16	4.47	4.39351458814947	0.0764854118505262
17	4.61	4.55290475047395	0.0570952495260492
18	4.65	4.7540901647934	-0.104090164793398
19	4.69	4.61523711211532	0.0747628878846814
20	4.82	4.77591149621634	0.0440885037836637
21	4.86	4.97828863788755	-0.118288637887551
22	4.87	4.77908375669744	0.090916243302561
23	5.01	4.99227453706753	0.0177254629324665
24	5.03	5.0533771179985	-0.0233771179985028
25	5.13	4.98008889758193	0.149911102418068
26	5.18	5.22656380431092	-0.0465638043109186
27	5.21	5.04739569089014	0.162604309109864
28	5.26	5.16935056124637	0.0906494387536255
29	5.25	5.25638137412178	-0.00638137412177796
30	5.2	5.2136861464644	-0.0136861464643993
31	5.16	5.12181714155653	0.0381828584434746
32	5.19	5.15290693955938	0.0370930604406221
33	5.39	5.26946412099723	0.120535879002774
34	5.58	5.51833413616976	0.0616658638302374
35	5.76	5.7879342242742	-0.0279342242741952
36	5.89	5.79611885560434	0.0938811443956576
37	5.98	5.97831210322602	0.00168789677398566
38	6.02	6.00796088311545	0.0120391168845495
39	5.62	5.87659155038924	-0.256591550389236
40	4.87	5.11031424241345	-0.24031424241345
41	4.24	4.25879972372338	-0.0187997237233816
42	4.02	3.89147916656808	0.128520833431921
43	3.74	3.89068697139791	-0.150686971397912
44	3.45	3.43317838013479	0.0168216198652134
45	3.34	3.28901684960328	0.0509831503967224
46	3.21	3.2510692612549	-0.0410692612549018
47	3.12	3.16139269621436	-0.0413926962143606
48	3.04	2.98201228014275	0.057987719857251

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2.97 & 3.05321092845207 & -0.0832109284520703 \tabularnewline
2 & 3.02 & 3.02167115517124 & -0.00167115517124363 \tabularnewline
3 & 3.07 & 3.01541325836723 & 0.0545867416327652 \tabularnewline
4 & 3.18 & 3.1068206081907 & 0.0731793918092982 \tabularnewline
5 & 3.29 & 3.32191415168089 & -0.0319141516808896 \tabularnewline
6 & 3.43 & 3.44074452217412 & -0.0107445221741233 \tabularnewline
7 & 3.61 & 3.57225877493024 & 0.0377412250697556 \tabularnewline
8 & 3.74 & 3.8380031840895 & -0.0980031840894992 \tabularnewline
9 & 3.87 & 3.92323039151194 & -0.0532303915119448 \tabularnewline
10 & 3.88 & 3.9915128458779 & -0.111512845877897 \tabularnewline
11 & 4.09 & 4.03839854244391 & 0.0516014575560893 \tabularnewline
12 & 4.19 & 4.31849174625441 & -0.128491746254406 \tabularnewline
13 & 4.2 & 4.26838807073998 & -0.0683880707399832 \tabularnewline
14 & 4.29 & 4.25380415740239 & 0.0361958425976128 \tabularnewline
15 & 4.37 & 4.33059950035339 & 0.0394004996466066 \tabularnewline
16 & 4.47 & 4.39351458814947 & 0.0764854118505262 \tabularnewline
17 & 4.61 & 4.55290475047395 & 0.0570952495260492 \tabularnewline
18 & 4.65 & 4.7540901647934 & -0.104090164793398 \tabularnewline
19 & 4.69 & 4.61523711211532 & 0.0747628878846814 \tabularnewline
20 & 4.82 & 4.77591149621634 & 0.0440885037836637 \tabularnewline
21 & 4.86 & 4.97828863788755 & -0.118288637887551 \tabularnewline
22 & 4.87 & 4.77908375669744 & 0.090916243302561 \tabularnewline
23 & 5.01 & 4.99227453706753 & 0.0177254629324665 \tabularnewline
24 & 5.03 & 5.0533771179985 & -0.0233771179985028 \tabularnewline
25 & 5.13 & 4.98008889758193 & 0.149911102418068 \tabularnewline
26 & 5.18 & 5.22656380431092 & -0.0465638043109186 \tabularnewline
27 & 5.21 & 5.04739569089014 & 0.162604309109864 \tabularnewline
28 & 5.26 & 5.16935056124637 & 0.0906494387536255 \tabularnewline
29 & 5.25 & 5.25638137412178 & -0.00638137412177796 \tabularnewline
30 & 5.2 & 5.2136861464644 & -0.0136861464643993 \tabularnewline
31 & 5.16 & 5.12181714155653 & 0.0381828584434746 \tabularnewline
32 & 5.19 & 5.15290693955938 & 0.0370930604406221 \tabularnewline
33 & 5.39 & 5.26946412099723 & 0.120535879002774 \tabularnewline
34 & 5.58 & 5.51833413616976 & 0.0616658638302374 \tabularnewline
35 & 5.76 & 5.7879342242742 & -0.0279342242741952 \tabularnewline
36 & 5.89 & 5.79611885560434 & 0.0938811443956576 \tabularnewline
37 & 5.98 & 5.97831210322602 & 0.00168789677398566 \tabularnewline
38 & 6.02 & 6.00796088311545 & 0.0120391168845495 \tabularnewline
39 & 5.62 & 5.87659155038924 & -0.256591550389236 \tabularnewline
40 & 4.87 & 5.11031424241345 & -0.24031424241345 \tabularnewline
41 & 4.24 & 4.25879972372338 & -0.0187997237233816 \tabularnewline
42 & 4.02 & 3.89147916656808 & 0.128520833431921 \tabularnewline
43 & 3.74 & 3.89068697139791 & -0.150686971397912 \tabularnewline
44 & 3.45 & 3.43317838013479 & 0.0168216198652134 \tabularnewline
45 & 3.34 & 3.28901684960328 & 0.0509831503967224 \tabularnewline
46 & 3.21 & 3.2510692612549 & -0.0410692612549018 \tabularnewline
47 & 3.12 & 3.16139269621436 & -0.0413926962143606 \tabularnewline
48 & 3.04 & 2.98201228014275 & 0.057987719857251 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57517&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]2.97[/C][C]3.05321092845207[/C][C]-0.0832109284520703[/C][/ROW]
[ROW][C]2[/C][C]3.02[/C][C]3.02167115517124[/C][C]-0.00167115517124363[/C][/ROW]
[ROW][C]3[/C][C]3.07[/C][C]3.01541325836723[/C][C]0.0545867416327652[/C][/ROW]
[ROW][C]4[/C][C]3.18[/C][C]3.1068206081907[/C][C]0.0731793918092982[/C][/ROW]
[ROW][C]5[/C][C]3.29[/C][C]3.32191415168089[/C][C]-0.0319141516808896[/C][/ROW]
[ROW][C]6[/C][C]3.43[/C][C]3.44074452217412[/C][C]-0.0107445221741233[/C][/ROW]
[ROW][C]7[/C][C]3.61[/C][C]3.57225877493024[/C][C]0.0377412250697556[/C][/ROW]
[ROW][C]8[/C][C]3.74[/C][C]3.8380031840895[/C][C]-0.0980031840894992[/C][/ROW]
[ROW][C]9[/C][C]3.87[/C][C]3.92323039151194[/C][C]-0.0532303915119448[/C][/ROW]
[ROW][C]10[/C][C]3.88[/C][C]3.9915128458779[/C][C]-0.111512845877897[/C][/ROW]
[ROW][C]11[/C][C]4.09[/C][C]4.03839854244391[/C][C]0.0516014575560893[/C][/ROW]
[ROW][C]12[/C][C]4.19[/C][C]4.31849174625441[/C][C]-0.128491746254406[/C][/ROW]
[ROW][C]13[/C][C]4.2[/C][C]4.26838807073998[/C][C]-0.0683880707399832[/C][/ROW]
[ROW][C]14[/C][C]4.29[/C][C]4.25380415740239[/C][C]0.0361958425976128[/C][/ROW]
[ROW][C]15[/C][C]4.37[/C][C]4.33059950035339[/C][C]0.0394004996466066[/C][/ROW]
[ROW][C]16[/C][C]4.47[/C][C]4.39351458814947[/C][C]0.0764854118505262[/C][/ROW]
[ROW][C]17[/C][C]4.61[/C][C]4.55290475047395[/C][C]0.0570952495260492[/C][/ROW]
[ROW][C]18[/C][C]4.65[/C][C]4.7540901647934[/C][C]-0.104090164793398[/C][/ROW]
[ROW][C]19[/C][C]4.69[/C][C]4.61523711211532[/C][C]0.0747628878846814[/C][/ROW]
[ROW][C]20[/C][C]4.82[/C][C]4.77591149621634[/C][C]0.0440885037836637[/C][/ROW]
[ROW][C]21[/C][C]4.86[/C][C]4.97828863788755[/C][C]-0.118288637887551[/C][/ROW]
[ROW][C]22[/C][C]4.87[/C][C]4.77908375669744[/C][C]0.090916243302561[/C][/ROW]
[ROW][C]23[/C][C]5.01[/C][C]4.99227453706753[/C][C]0.0177254629324665[/C][/ROW]
[ROW][C]24[/C][C]5.03[/C][C]5.0533771179985[/C][C]-0.0233771179985028[/C][/ROW]
[ROW][C]25[/C][C]5.13[/C][C]4.98008889758193[/C][C]0.149911102418068[/C][/ROW]
[ROW][C]26[/C][C]5.18[/C][C]5.22656380431092[/C][C]-0.0465638043109186[/C][/ROW]
[ROW][C]27[/C][C]5.21[/C][C]5.04739569089014[/C][C]0.162604309109864[/C][/ROW]
[ROW][C]28[/C][C]5.26[/C][C]5.16935056124637[/C][C]0.0906494387536255[/C][/ROW]
[ROW][C]29[/C][C]5.25[/C][C]5.25638137412178[/C][C]-0.00638137412177796[/C][/ROW]
[ROW][C]30[/C][C]5.2[/C][C]5.2136861464644[/C][C]-0.0136861464643993[/C][/ROW]
[ROW][C]31[/C][C]5.16[/C][C]5.12181714155653[/C][C]0.0381828584434746[/C][/ROW]
[ROW][C]32[/C][C]5.19[/C][C]5.15290693955938[/C][C]0.0370930604406221[/C][/ROW]
[ROW][C]33[/C][C]5.39[/C][C]5.26946412099723[/C][C]0.120535879002774[/C][/ROW]
[ROW][C]34[/C][C]5.58[/C][C]5.51833413616976[/C][C]0.0616658638302374[/C][/ROW]
[ROW][C]35[/C][C]5.76[/C][C]5.7879342242742[/C][C]-0.0279342242741952[/C][/ROW]
[ROW][C]36[/C][C]5.89[/C][C]5.79611885560434[/C][C]0.0938811443956576[/C][/ROW]
[ROW][C]37[/C][C]5.98[/C][C]5.97831210322602[/C][C]0.00168789677398566[/C][/ROW]
[ROW][C]38[/C][C]6.02[/C][C]6.00796088311545[/C][C]0.0120391168845495[/C][/ROW]
[ROW][C]39[/C][C]5.62[/C][C]5.87659155038924[/C][C]-0.256591550389236[/C][/ROW]
[ROW][C]40[/C][C]4.87[/C][C]5.11031424241345[/C][C]-0.24031424241345[/C][/ROW]
[ROW][C]41[/C][C]4.24[/C][C]4.25879972372338[/C][C]-0.0187997237233816[/C][/ROW]
[ROW][C]42[/C][C]4.02[/C][C]3.89147916656808[/C][C]0.128520833431921[/C][/ROW]
[ROW][C]43[/C][C]3.74[/C][C]3.89068697139791[/C][C]-0.150686971397912[/C][/ROW]
[ROW][C]44[/C][C]3.45[/C][C]3.43317838013479[/C][C]0.0168216198652134[/C][/ROW]
[ROW][C]45[/C][C]3.34[/C][C]3.28901684960328[/C][C]0.0509831503967224[/C][/ROW]
[ROW][C]46[/C][C]3.21[/C][C]3.2510692612549[/C][C]-0.0410692612549018[/C][/ROW]
[ROW][C]47[/C][C]3.12[/C][C]3.16139269621436[/C][C]-0.0413926962143606[/C][/ROW]
[ROW][C]48[/C][C]3.04[/C][C]2.98201228014275[/C][C]0.057987719857251[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57517&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57517&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	2.97	3.05321092845207	-0.0832109284520703
2	3.02	3.02167115517124	-0.00167115517124363
3	3.07	3.01541325836723	0.0545867416327652
4	3.18	3.1068206081907	0.0731793918092982
5	3.29	3.32191415168089	-0.0319141516808896
6	3.43	3.44074452217412	-0.0107445221741233
7	3.61	3.57225877493024	0.0377412250697556
8	3.74	3.8380031840895	-0.0980031840894992
9	3.87	3.92323039151194	-0.0532303915119448
10	3.88	3.9915128458779	-0.111512845877897
11	4.09	4.03839854244391	0.0516014575560893
12	4.19	4.31849174625441	-0.128491746254406
13	4.2	4.26838807073998	-0.0683880707399832
14	4.29	4.25380415740239	0.0361958425976128
15	4.37	4.33059950035339	0.0394004996466066
16	4.47	4.39351458814947	0.0764854118505262
17	4.61	4.55290475047395	0.0570952495260492
18	4.65	4.7540901647934	-0.104090164793398
19	4.69	4.61523711211532	0.0747628878846814
20	4.82	4.77591149621634	0.0440885037836637
21	4.86	4.97828863788755	-0.118288637887551
22	4.87	4.77908375669744	0.090916243302561
23	5.01	4.99227453706753	0.0177254629324665
24	5.03	5.0533771179985	-0.0233771179985028
25	5.13	4.98008889758193	0.149911102418068
26	5.18	5.22656380431092	-0.0465638043109186
27	5.21	5.04739569089014	0.162604309109864
28	5.26	5.16935056124637	0.0906494387536255
29	5.25	5.25638137412178	-0.00638137412177796
30	5.2	5.2136861464644	-0.0136861464643993
31	5.16	5.12181714155653	0.0381828584434746
32	5.19	5.15290693955938	0.0370930604406221
33	5.39	5.26946412099723	0.120535879002774
34	5.58	5.51833413616976	0.0616658638302374
35	5.76	5.7879342242742	-0.0279342242741952
36	5.89	5.79611885560434	0.0938811443956576
37	5.98	5.97831210322602	0.00168789677398566
38	6.02	6.00796088311545	0.0120391168845495
39	5.62	5.87659155038924	-0.256591550389236
40	4.87	5.11031424241345	-0.24031424241345
41	4.24	4.25879972372338	-0.0187997237233816
42	4.02	3.89147916656808	0.128520833431921
43	3.74	3.89068697139791	-0.150686971397912
44	3.45	3.43317838013479	0.0168216198652134
45	3.34	3.28901684960328	0.0509831503967224
46	3.21	3.2510692612549	-0.0410692612549018
47	3.12	3.16139269621436	-0.0413926962143606
48	3.04	2.98201228014275	0.057987719857251

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
22	0.0972645976134416	0.194529195226883	0.902735402386558
23	0.0703816052642715	0.140763210528543	0.929618394735728
24	0.0246872633953323	0.0493745267906646	0.975312736604668
25	0.0334800415267999	0.0669600830535999	0.9665199584732
26	0.188825354837066	0.377650709674132	0.811174645162934

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
22 & 0.0972645976134416 & 0.194529195226883 & 0.902735402386558 \tabularnewline
23 & 0.0703816052642715 & 0.140763210528543 & 0.929618394735728 \tabularnewline
24 & 0.0246872633953323 & 0.0493745267906646 & 0.975312736604668 \tabularnewline
25 & 0.0334800415267999 & 0.0669600830535999 & 0.9665199584732 \tabularnewline
26 & 0.188825354837066 & 0.377650709674132 & 0.811174645162934 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57517&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]22[/C][C]0.0972645976134416[/C][C]0.194529195226883[/C][C]0.902735402386558[/C][/ROW]
[ROW][C]23[/C][C]0.0703816052642715[/C][C]0.140763210528543[/C][C]0.929618394735728[/C][/ROW]
[ROW][C]24[/C][C]0.0246872633953323[/C][C]0.0493745267906646[/C][C]0.975312736604668[/C][/ROW]
[ROW][C]25[/C][C]0.0334800415267999[/C][C]0.0669600830535999[/C][C]0.9665199584732[/C][/ROW]
[ROW][C]26[/C][C]0.188825354837066[/C][C]0.377650709674132[/C][C]0.811174645162934[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57517&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57517&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
22	0.0972645976134416	0.194529195226883	0.902735402386558
23	0.0703816052642715	0.140763210528543	0.929618394735728
24	0.0246872633953323	0.0493745267906646	0.975312736604668
25	0.0334800415267999	0.0669600830535999	0.9665199584732
26	0.188825354837066	0.377650709674132	0.811174645162934

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	1	0.2	NOK
10% type I error level	2	0.4	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 & 1 & 0.2 & NOK \tabularnewline
10% type I error level & 2 & 0.4 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57517&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]1[/C][C]0.2[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.4[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57517&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57517&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	1	0.2	NOK
10% type I error level	2	0.4	NOK

Figure 1

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

R code (references can be found in the software module):

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$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