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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Thu, 31 Jan 2019 15:38:42 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2019/Jan/31/t1548945658e15j0fo0i1hioku.htm/, Retrieved Sun, 05 May 2024 17:59:58 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=318153, Retrieved Sun, 05 May 2024 17:59:58 +0000

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-       [Multiple Regression] [] [2019-01-31 14:38:42] [23a8c4e2b29930b56218fe862c106e41] [Current]

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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	12 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time12 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318153&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

12 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=318153&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318153&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	12 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
[t] = + 3322.01 + 0.128828`F1C(t-1)`[t] -0.248037`F1C(t-2)`[t] + 0.0153302`F1C(t-3)`[t] -0.0929215`F1C(t-4)`[t] -0.0976214`F1C(t-5)`[t] -0.0475415`F1C(t-6)`[t] -0.0798657`F1C(t-7)`[t] + 0.0636949`F1C(t-8)`[t] -0.00833499`F1C(t-9)`[t] + 0.222908`F1C(t-10)`[t] + 0.00887549`F1C(t-11)`[t] -0.283728`F1C(t-12)`[t] + 489.948M1[t] + 781.735M2[t] + 746.321M3[t] + 258.598M4[t] -150.416M5[t] -339.192M6[t] -463.056M7[t] -808.364M8[t] -990.457M9[t] -921.372M10[t] -868.529M11[t] -7.77076t + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
[t] =  +  3322.01 +  0.128828`F1C(t-1)`[t] -0.248037`F1C(t-2)`[t] +  0.0153302`F1C(t-3)`[t] -0.0929215`F1C(t-4)`[t] -0.0976214`F1C(t-5)`[t] -0.0475415`F1C(t-6)`[t] -0.0798657`F1C(t-7)`[t] +  0.0636949`F1C(t-8)`[t] -0.00833499`F1C(t-9)`[t] +  0.222908`F1C(t-10)`[t] +  0.00887549`F1C(t-11)`[t] -0.283728`F1C(t-12)`[t] +  489.948M1[t] +  781.735M2[t] +  746.321M3[t] +  258.598M4[t] -150.416M5[t] -339.192M6[t] -463.056M7[t] -808.364M8[t] -990.457M9[t] -921.372M10[t] -868.529M11[t] -7.77076t  + e[t] \tabularnewline
Warning: you did not specify the column number of the endogenous series! The first column was selected by default. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318153&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C][t] =  +  3322.01 +  0.128828`F1C(t-1)`[t] -0.248037`F1C(t-2)`[t] +  0.0153302`F1C(t-3)`[t] -0.0929215`F1C(t-4)`[t] -0.0976214`F1C(t-5)`[t] -0.0475415`F1C(t-6)`[t] -0.0798657`F1C(t-7)`[t] +  0.0636949`F1C(t-8)`[t] -0.00833499`F1C(t-9)`[t] +  0.222908`F1C(t-10)`[t] +  0.00887549`F1C(t-11)`[t] -0.283728`F1C(t-12)`[t] +  489.948M1[t] +  781.735M2[t] +  746.321M3[t] +  258.598M4[t] -150.416M5[t] -339.192M6[t] -463.056M7[t] -808.364M8[t] -990.457M9[t] -921.372M10[t] -868.529M11[t] -7.77076t  + e[t][/C][/ROW]
[ROW][C]Warning: you did not specify the column number of the endogenous series! The first column was selected by default.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318153&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318153&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
[t] = + 3322.01 + 0.128828`F1C(t-1)`[t] -0.248037`F1C(t-2)`[t] + 0.0153302`F1C(t-3)`[t] -0.0929215`F1C(t-4)`[t] -0.0976214`F1C(t-5)`[t] -0.0475415`F1C(t-6)`[t] -0.0798657`F1C(t-7)`[t] + 0.0636949`F1C(t-8)`[t] -0.00833499`F1C(t-9)`[t] + 0.222908`F1C(t-10)`[t] + 0.00887549`F1C(t-11)`[t] -0.283728`F1C(t-12)`[t] + 489.948M1[t] + 781.735M2[t] + 746.321M3[t] + 258.598M4[t] -150.416M5[t] -339.192M6[t] -463.056M7[t] -808.364M8[t] -990.457M9[t] -921.372M10[t] -868.529M11[t] -7.77076t + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+3322	1546	+2.1480e+00	0.0387	0.01935
`F1C(t-1)`	+0.1288	0.1688	+7.6330e-01	0.4504	0.2252
`F1C(t-2)`	-0.248	0.1685	-1.4720e+00	0.15	0.07498
`F1C(t-3)`	+0.01533	0.1702	+9.0090e-02	0.9287	0.4644
`F1C(t-4)`	-0.09292	0.1713	-5.4260e-01	0.5909	0.2954
`F1C(t-5)`	-0.09762	0.1721	-5.6740e-01	0.5741	0.287
`F1C(t-6)`	-0.04754	0.1724	-2.7570e-01	0.7844	0.3922
`F1C(t-7)`	-0.07987	0.1722	-4.6390e-01	0.6456	0.3228
`F1C(t-8)`	+0.0637	0.1725	+3.6930e-01	0.7141	0.3571
`F1C(t-9)`	-0.008335	0.1725	-4.8320e-02	0.9617	0.4809
`F1C(t-10)`	+0.2229	0.1724	+1.2930e+00	0.2044	0.1022
`F1C(t-11)`	+0.008875	0.1616	+5.4920e-02	0.9565	0.4783
`F1C(t-12)`	-0.2837	0.1613	-1.7590e+00	0.08727	0.04363
M1	+489.9	249	+1.9680e+00	0.05707	0.02854
M2	+781.7	382.6	+2.0430e+00	0.04861	0.0243
M3	+746.3	516.2	+1.4460e+00	0.1571	0.07857
M4	+258.6	607.9	+4.2540e-01	0.6732	0.3366
M5	-150.4	639	-2.3540e-01	0.8153	0.4076
M6	-339.2	624.2	-5.4340e-01	0.5903	0.2951
M7	-463.1	584.5	-7.9230e-01	0.4335	0.2168
M8	-808.4	525.9	-1.5370e+00	0.1332	0.06662
M9	-990.5	435.9	-2.2720e+00	0.02932	0.01466
M10	-921.4	318.2	-2.8960e+00	0.006479	0.00324
M11	-868.5	213	-4.0780e+00	0.0002488	0.0001244
t	-7.771	4.247	-1.8300e+00	0.0758	0.0379

\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) & +3322 &  1546 & +2.1480e+00 &  0.0387 &  0.01935 \tabularnewline
`F1C(t-1)` & +0.1288 &  0.1688 & +7.6330e-01 &  0.4504 &  0.2252 \tabularnewline
`F1C(t-2)` & -0.248 &  0.1685 & -1.4720e+00 &  0.15 &  0.07498 \tabularnewline
`F1C(t-3)` & +0.01533 &  0.1702 & +9.0090e-02 &  0.9287 &  0.4644 \tabularnewline
`F1C(t-4)` & -0.09292 &  0.1713 & -5.4260e-01 &  0.5909 &  0.2954 \tabularnewline
`F1C(t-5)` & -0.09762 &  0.1721 & -5.6740e-01 &  0.5741 &  0.287 \tabularnewline
`F1C(t-6)` & -0.04754 &  0.1724 & -2.7570e-01 &  0.7844 &  0.3922 \tabularnewline
`F1C(t-7)` & -0.07987 &  0.1722 & -4.6390e-01 &  0.6456 &  0.3228 \tabularnewline
`F1C(t-8)` & +0.0637 &  0.1725 & +3.6930e-01 &  0.7141 &  0.3571 \tabularnewline
`F1C(t-9)` & -0.008335 &  0.1725 & -4.8320e-02 &  0.9617 &  0.4809 \tabularnewline
`F1C(t-10)` & +0.2229 &  0.1724 & +1.2930e+00 &  0.2044 &  0.1022 \tabularnewline
`F1C(t-11)` & +0.008875 &  0.1616 & +5.4920e-02 &  0.9565 &  0.4783 \tabularnewline
`F1C(t-12)` & -0.2837 &  0.1613 & -1.7590e+00 &  0.08727 &  0.04363 \tabularnewline
M1 & +489.9 &  249 & +1.9680e+00 &  0.05707 &  0.02854 \tabularnewline
M2 & +781.7 &  382.6 & +2.0430e+00 &  0.04861 &  0.0243 \tabularnewline
M3 & +746.3 &  516.2 & +1.4460e+00 &  0.1571 &  0.07857 \tabularnewline
M4 & +258.6 &  607.9 & +4.2540e-01 &  0.6732 &  0.3366 \tabularnewline
M5 & -150.4 &  639 & -2.3540e-01 &  0.8153 &  0.4076 \tabularnewline
M6 & -339.2 &  624.2 & -5.4340e-01 &  0.5903 &  0.2951 \tabularnewline
M7 & -463.1 &  584.5 & -7.9230e-01 &  0.4335 &  0.2168 \tabularnewline
M8 & -808.4 &  525.9 & -1.5370e+00 &  0.1332 &  0.06662 \tabularnewline
M9 & -990.5 &  435.9 & -2.2720e+00 &  0.02932 &  0.01466 \tabularnewline
M10 & -921.4 &  318.2 & -2.8960e+00 &  0.006479 &  0.00324 \tabularnewline
M11 & -868.5 &  213 & -4.0780e+00 &  0.0002488 &  0.0001244 \tabularnewline
t & -7.771 &  4.247 & -1.8300e+00 &  0.0758 &  0.0379 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318153&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]+3322[/C][C] 1546[/C][C]+2.1480e+00[/C][C] 0.0387[/C][C] 0.01935[/C][/ROW]
[ROW][C]`F1C(t-1)`[/C][C]+0.1288[/C][C] 0.1688[/C][C]+7.6330e-01[/C][C] 0.4504[/C][C] 0.2252[/C][/ROW]
[ROW][C]`F1C(t-2)`[/C][C]-0.248[/C][C] 0.1685[/C][C]-1.4720e+00[/C][C] 0.15[/C][C] 0.07498[/C][/ROW]
[ROW][C]`F1C(t-3)`[/C][C]+0.01533[/C][C] 0.1702[/C][C]+9.0090e-02[/C][C] 0.9287[/C][C] 0.4644[/C][/ROW]
[ROW][C]`F1C(t-4)`[/C][C]-0.09292[/C][C] 0.1713[/C][C]-5.4260e-01[/C][C] 0.5909[/C][C] 0.2954[/C][/ROW]
[ROW][C]`F1C(t-5)`[/C][C]-0.09762[/C][C] 0.1721[/C][C]-5.6740e-01[/C][C] 0.5741[/C][C] 0.287[/C][/ROW]
[ROW][C]`F1C(t-6)`[/C][C]-0.04754[/C][C] 0.1724[/C][C]-2.7570e-01[/C][C] 0.7844[/C][C] 0.3922[/C][/ROW]
[ROW][C]`F1C(t-7)`[/C][C]-0.07987[/C][C] 0.1722[/C][C]-4.6390e-01[/C][C] 0.6456[/C][C] 0.3228[/C][/ROW]
[ROW][C]`F1C(t-8)`[/C][C]+0.0637[/C][C] 0.1725[/C][C]+3.6930e-01[/C][C] 0.7141[/C][C] 0.3571[/C][/ROW]
[ROW][C]`F1C(t-9)`[/C][C]-0.008335[/C][C] 0.1725[/C][C]-4.8320e-02[/C][C] 0.9617[/C][C] 0.4809[/C][/ROW]
[ROW][C]`F1C(t-10)`[/C][C]+0.2229[/C][C] 0.1724[/C][C]+1.2930e+00[/C][C] 0.2044[/C][C] 0.1022[/C][/ROW]
[ROW][C]`F1C(t-11)`[/C][C]+0.008875[/C][C] 0.1616[/C][C]+5.4920e-02[/C][C] 0.9565[/C][C] 0.4783[/C][/ROW]
[ROW][C]`F1C(t-12)`[/C][C]-0.2837[/C][C] 0.1613[/C][C]-1.7590e+00[/C][C] 0.08727[/C][C] 0.04363[/C][/ROW]
[ROW][C]M1[/C][C]+489.9[/C][C] 249[/C][C]+1.9680e+00[/C][C] 0.05707[/C][C] 0.02854[/C][/ROW]
[ROW][C]M2[/C][C]+781.7[/C][C] 382.6[/C][C]+2.0430e+00[/C][C] 0.04861[/C][C] 0.0243[/C][/ROW]
[ROW][C]M3[/C][C]+746.3[/C][C] 516.2[/C][C]+1.4460e+00[/C][C] 0.1571[/C][C] 0.07857[/C][/ROW]
[ROW][C]M4[/C][C]+258.6[/C][C] 607.9[/C][C]+4.2540e-01[/C][C] 0.6732[/C][C] 0.3366[/C][/ROW]
[ROW][C]M5[/C][C]-150.4[/C][C] 639[/C][C]-2.3540e-01[/C][C] 0.8153[/C][C] 0.4076[/C][/ROW]
[ROW][C]M6[/C][C]-339.2[/C][C] 624.2[/C][C]-5.4340e-01[/C][C] 0.5903[/C][C] 0.2951[/C][/ROW]
[ROW][C]M7[/C][C]-463.1[/C][C] 584.5[/C][C]-7.9230e-01[/C][C] 0.4335[/C][C] 0.2168[/C][/ROW]
[ROW][C]M8[/C][C]-808.4[/C][C] 525.9[/C][C]-1.5370e+00[/C][C] 0.1332[/C][C] 0.06662[/C][/ROW]
[ROW][C]M9[/C][C]-990.5[/C][C] 435.9[/C][C]-2.2720e+00[/C][C] 0.02932[/C][C] 0.01466[/C][/ROW]
[ROW][C]M10[/C][C]-921.4[/C][C] 318.2[/C][C]-2.8960e+00[/C][C] 0.006479[/C][C] 0.00324[/C][/ROW]
[ROW][C]M11[/C][C]-868.5[/C][C] 213[/C][C]-4.0780e+00[/C][C] 0.0002488[/C][C] 0.0001244[/C][/ROW]
[ROW][C]t[/C][C]-7.771[/C][C] 4.247[/C][C]-1.8300e+00[/C][C] 0.0758[/C][C] 0.0379[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318153&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318153&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)	+3322	1546	+2.1480e+00	0.0387	0.01935
`F1C(t-1)`	+0.1288	0.1688	+7.6330e-01	0.4504	0.2252
`F1C(t-2)`	-0.248	0.1685	-1.4720e+00	0.15	0.07498
`F1C(t-3)`	+0.01533	0.1702	+9.0090e-02	0.9287	0.4644
`F1C(t-4)`	-0.09292	0.1713	-5.4260e-01	0.5909	0.2954
`F1C(t-5)`	-0.09762	0.1721	-5.6740e-01	0.5741	0.287
`F1C(t-6)`	-0.04754	0.1724	-2.7570e-01	0.7844	0.3922
`F1C(t-7)`	-0.07987	0.1722	-4.6390e-01	0.6456	0.3228
`F1C(t-8)`	+0.0637	0.1725	+3.6930e-01	0.7141	0.3571
`F1C(t-9)`	-0.008335	0.1725	-4.8320e-02	0.9617	0.4809
`F1C(t-10)`	+0.2229	0.1724	+1.2930e+00	0.2044	0.1022
`F1C(t-11)`	+0.008875	0.1616	+5.4920e-02	0.9565	0.4783
`F1C(t-12)`	-0.2837	0.1613	-1.7590e+00	0.08727	0.04363
M1	+489.9	249	+1.9680e+00	0.05707	0.02854
M2	+781.7	382.6	+2.0430e+00	0.04861	0.0243
M3	+746.3	516.2	+1.4460e+00	0.1571	0.07857
M4	+258.6	607.9	+4.2540e-01	0.6732	0.3366
M5	-150.4	639	-2.3540e-01	0.8153	0.4076
M6	-339.2	624.2	-5.4340e-01	0.5903	0.2951
M7	-463.1	584.5	-7.9230e-01	0.4335	0.2168
M8	-808.4	525.9	-1.5370e+00	0.1332	0.06662
M9	-990.5	435.9	-2.2720e+00	0.02932	0.01466
M10	-921.4	318.2	-2.8960e+00	0.006479	0.00324
M11	-868.5	213	-4.0780e+00	0.0002488	0.0001244
t	-7.771	4.247	-1.8300e+00	0.0758	0.0379

Multiple Linear Regression - Regression Statistics
Multiple R	0.959
R-squared	0.9196
Adjusted R-squared	0.8645
F-TEST (value)	16.69
F-TEST (DF numerator)	24
F-TEST (DF denominator)	35
p-value	7.895e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	232.3
Sum Squared Residuals	1.889e+06

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.959 \tabularnewline
R-squared &  0.9196 \tabularnewline
Adjusted R-squared &  0.8645 \tabularnewline
F-TEST (value) &  16.69 \tabularnewline
F-TEST (DF numerator) & 24 \tabularnewline
F-TEST (DF denominator) & 35 \tabularnewline
p-value &  7.895e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  232.3 \tabularnewline
Sum Squared Residuals &  1.889e+06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318153&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.959[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.9196[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.8645[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 16.69[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]24[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]35[/C][/ROW]
[ROW][C]p-value[/C][C] 7.895e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 232.3[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1.889e+06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318153&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318153&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	0.959
R-squared	0.9196
Adjusted R-squared	0.8645
F-TEST (value)	16.69
F-TEST (DF numerator)	24
F-TEST (DF denominator)	35
p-value	7.895e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	232.3
Sum Squared Residuals	1.889e+06

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

\begin{tabular}{lllllllll}
\hline
Menu of Residual Diagnostics \tabularnewline
Description & Link \tabularnewline
Histogram & Compute \tabularnewline
Central Tendency & Compute \tabularnewline
QQ Plot & Compute \tabularnewline
Kernel Density Plot & Compute \tabularnewline
Skewness/Kurtosis Test & Compute \tabularnewline
Skewness-Kurtosis Plot & Compute \tabularnewline
Harrell-Davis Plot & Compute \tabularnewline
Bootstrap Plot -- Central Tendency & Compute \tabularnewline
Blocked Bootstrap Plot -- Central Tendency & Compute \tabularnewline
(Partial) Autocorrelation Plot & Compute \tabularnewline
Spectral Analysis & Compute \tabularnewline
Tukey lambda PPCC Plot & Compute \tabularnewline
Box-Cox Normality Plot & Compute \tabularnewline
Summary Statistics & Compute \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318153&T=4

[TABLE]
[ROW][C]Menu of Residual Diagnostics[/C][/ROW]
[ROW][C]Description[/C][C]Link[/C][/ROW]
[ROW][C]Histogram[/C][C]Compute[/C][/ROW]
[ROW][C]Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]QQ Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Kernel Density Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness/Kurtosis Test[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness-Kurtosis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Harrell-Davis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]Blocked Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C](Partial) Autocorrelation Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Spectral Analysis[/C][C]Compute[/C][/ROW]
[ROW][C]Tukey lambda PPCC Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Box-Cox Normality Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Summary Statistics[/C][C]Compute[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318153&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318153&T=4

As an alternative you can also use a QR Code:

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

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	2933	2974	-41.36
2	2889	3276	-387.1
3	2938	2924	13.95
4	2497	2327	169.9
5	1870	1898	-27.87
6	1726	1757	-31.18
7	1607	1710	-103
8	1545	1554	-9.376
9	1396	1522	-126.2
10	1787	1598	188.5
11	2076	1885	191.3
12	2837	2634	202.6
13	2787	3041	-253.8
14	3891	3035	855.6
15	3179	2942	237.5
16	2011	2059	-47.58
17	1636	1711	-75.36
18	1580	1640	-60.46
19	1489	1492	-2.853
20	1300	1414	-114.2
21	1356	1417	-60.91
22	1653	1817	-164.1
23	2013	1868	145.2
24	2823	2722	100.6
25	3102	3097	4.625
26	2294	2625	-330.8
27	2385	2473	-88.43
28	2444	2370	74.35
29	1748	1870	-122.4
30	1554	1543	11.03
31	1498	1609	-111.4
32	1361	1457	-96.27
33	1346	1458	-111.8
34	1564	1672	-107.9
35	1640	1779	-139
36	2293	2274	19.24
37	2815	2747	68.39
38	3137	3166	-28.92
39	2679	2846	-166.7
40	1969	2068	-98.63
41	1870	1728	141.9
42	1633	1608	25.1
43	1529	1408	121.1
44	1366	1281	85.32
45	1357	1217	139.5
46	1570	1519	50.96
47	1535	1754	-219.2
48	2491	2452	39.29
49	3084	2862	222.1
50	2605	2714	-108.8
51	2573	2569	3.669
52	2143	2241	-98.02
53	1693	1609	83.76
54	1504	1448	55.52
55	1461	1365	96.24
56	1354	1219	134.6
57	1333	1174	159.4
58	1492	1459	32.54
59	1781	1759	21.67
60	1915	2277	-361.7

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  2933 &  2974 & -41.36 \tabularnewline
2 &  2889 &  3276 & -387.1 \tabularnewline
3 &  2938 &  2924 &  13.95 \tabularnewline
4 &  2497 &  2327 &  169.9 \tabularnewline
5 &  1870 &  1898 & -27.87 \tabularnewline
6 &  1726 &  1757 & -31.18 \tabularnewline
7 &  1607 &  1710 & -103 \tabularnewline
8 &  1545 &  1554 & -9.376 \tabularnewline
9 &  1396 &  1522 & -126.2 \tabularnewline
10 &  1787 &  1598 &  188.5 \tabularnewline
11 &  2076 &  1885 &  191.3 \tabularnewline
12 &  2837 &  2634 &  202.6 \tabularnewline
13 &  2787 &  3041 & -253.8 \tabularnewline
14 &  3891 &  3035 &  855.6 \tabularnewline
15 &  3179 &  2942 &  237.5 \tabularnewline
16 &  2011 &  2059 & -47.58 \tabularnewline
17 &  1636 &  1711 & -75.36 \tabularnewline
18 &  1580 &  1640 & -60.46 \tabularnewline
19 &  1489 &  1492 & -2.853 \tabularnewline
20 &  1300 &  1414 & -114.2 \tabularnewline
21 &  1356 &  1417 & -60.91 \tabularnewline
22 &  1653 &  1817 & -164.1 \tabularnewline
23 &  2013 &  1868 &  145.2 \tabularnewline
24 &  2823 &  2722 &  100.6 \tabularnewline
25 &  3102 &  3097 &  4.625 \tabularnewline
26 &  2294 &  2625 & -330.8 \tabularnewline
27 &  2385 &  2473 & -88.43 \tabularnewline
28 &  2444 &  2370 &  74.35 \tabularnewline
29 &  1748 &  1870 & -122.4 \tabularnewline
30 &  1554 &  1543 &  11.03 \tabularnewline
31 &  1498 &  1609 & -111.4 \tabularnewline
32 &  1361 &  1457 & -96.27 \tabularnewline
33 &  1346 &  1458 & -111.8 \tabularnewline
34 &  1564 &  1672 & -107.9 \tabularnewline
35 &  1640 &  1779 & -139 \tabularnewline
36 &  2293 &  2274 &  19.24 \tabularnewline
37 &  2815 &  2747 &  68.39 \tabularnewline
38 &  3137 &  3166 & -28.92 \tabularnewline
39 &  2679 &  2846 & -166.7 \tabularnewline
40 &  1969 &  2068 & -98.63 \tabularnewline
41 &  1870 &  1728 &  141.9 \tabularnewline
42 &  1633 &  1608 &  25.1 \tabularnewline
43 &  1529 &  1408 &  121.1 \tabularnewline
44 &  1366 &  1281 &  85.32 \tabularnewline
45 &  1357 &  1217 &  139.5 \tabularnewline
46 &  1570 &  1519 &  50.96 \tabularnewline
47 &  1535 &  1754 & -219.2 \tabularnewline
48 &  2491 &  2452 &  39.29 \tabularnewline
49 &  3084 &  2862 &  222.1 \tabularnewline
50 &  2605 &  2714 & -108.8 \tabularnewline
51 &  2573 &  2569 &  3.669 \tabularnewline
52 &  2143 &  2241 & -98.02 \tabularnewline
53 &  1693 &  1609 &  83.76 \tabularnewline
54 &  1504 &  1448 &  55.52 \tabularnewline
55 &  1461 &  1365 &  96.24 \tabularnewline
56 &  1354 &  1219 &  134.6 \tabularnewline
57 &  1333 &  1174 &  159.4 \tabularnewline
58 &  1492 &  1459 &  32.54 \tabularnewline
59 &  1781 &  1759 &  21.67 \tabularnewline
60 &  1915 &  2277 & -361.7 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318153&T=5

[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] 2933[/C][C] 2974[/C][C]-41.36[/C][/ROW]
[ROW][C]2[/C][C] 2889[/C][C] 3276[/C][C]-387.1[/C][/ROW]
[ROW][C]3[/C][C] 2938[/C][C] 2924[/C][C] 13.95[/C][/ROW]
[ROW][C]4[/C][C] 2497[/C][C] 2327[/C][C] 169.9[/C][/ROW]
[ROW][C]5[/C][C] 1870[/C][C] 1898[/C][C]-27.87[/C][/ROW]
[ROW][C]6[/C][C] 1726[/C][C] 1757[/C][C]-31.18[/C][/ROW]
[ROW][C]7[/C][C] 1607[/C][C] 1710[/C][C]-103[/C][/ROW]
[ROW][C]8[/C][C] 1545[/C][C] 1554[/C][C]-9.376[/C][/ROW]
[ROW][C]9[/C][C] 1396[/C][C] 1522[/C][C]-126.2[/C][/ROW]
[ROW][C]10[/C][C] 1787[/C][C] 1598[/C][C] 188.5[/C][/ROW]
[ROW][C]11[/C][C] 2076[/C][C] 1885[/C][C] 191.3[/C][/ROW]
[ROW][C]12[/C][C] 2837[/C][C] 2634[/C][C] 202.6[/C][/ROW]
[ROW][C]13[/C][C] 2787[/C][C] 3041[/C][C]-253.8[/C][/ROW]
[ROW][C]14[/C][C] 3891[/C][C] 3035[/C][C] 855.6[/C][/ROW]
[ROW][C]15[/C][C] 3179[/C][C] 2942[/C][C] 237.5[/C][/ROW]
[ROW][C]16[/C][C] 2011[/C][C] 2059[/C][C]-47.58[/C][/ROW]
[ROW][C]17[/C][C] 1636[/C][C] 1711[/C][C]-75.36[/C][/ROW]
[ROW][C]18[/C][C] 1580[/C][C] 1640[/C][C]-60.46[/C][/ROW]
[ROW][C]19[/C][C] 1489[/C][C] 1492[/C][C]-2.853[/C][/ROW]
[ROW][C]20[/C][C] 1300[/C][C] 1414[/C][C]-114.2[/C][/ROW]
[ROW][C]21[/C][C] 1356[/C][C] 1417[/C][C]-60.91[/C][/ROW]
[ROW][C]22[/C][C] 1653[/C][C] 1817[/C][C]-164.1[/C][/ROW]
[ROW][C]23[/C][C] 2013[/C][C] 1868[/C][C] 145.2[/C][/ROW]
[ROW][C]24[/C][C] 2823[/C][C] 2722[/C][C] 100.6[/C][/ROW]
[ROW][C]25[/C][C] 3102[/C][C] 3097[/C][C] 4.625[/C][/ROW]
[ROW][C]26[/C][C] 2294[/C][C] 2625[/C][C]-330.8[/C][/ROW]
[ROW][C]27[/C][C] 2385[/C][C] 2473[/C][C]-88.43[/C][/ROW]
[ROW][C]28[/C][C] 2444[/C][C] 2370[/C][C] 74.35[/C][/ROW]
[ROW][C]29[/C][C] 1748[/C][C] 1870[/C][C]-122.4[/C][/ROW]
[ROW][C]30[/C][C] 1554[/C][C] 1543[/C][C] 11.03[/C][/ROW]
[ROW][C]31[/C][C] 1498[/C][C] 1609[/C][C]-111.4[/C][/ROW]
[ROW][C]32[/C][C] 1361[/C][C] 1457[/C][C]-96.27[/C][/ROW]
[ROW][C]33[/C][C] 1346[/C][C] 1458[/C][C]-111.8[/C][/ROW]
[ROW][C]34[/C][C] 1564[/C][C] 1672[/C][C]-107.9[/C][/ROW]
[ROW][C]35[/C][C] 1640[/C][C] 1779[/C][C]-139[/C][/ROW]
[ROW][C]36[/C][C] 2293[/C][C] 2274[/C][C] 19.24[/C][/ROW]
[ROW][C]37[/C][C] 2815[/C][C] 2747[/C][C] 68.39[/C][/ROW]
[ROW][C]38[/C][C] 3137[/C][C] 3166[/C][C]-28.92[/C][/ROW]
[ROW][C]39[/C][C] 2679[/C][C] 2846[/C][C]-166.7[/C][/ROW]
[ROW][C]40[/C][C] 1969[/C][C] 2068[/C][C]-98.63[/C][/ROW]
[ROW][C]41[/C][C] 1870[/C][C] 1728[/C][C] 141.9[/C][/ROW]
[ROW][C]42[/C][C] 1633[/C][C] 1608[/C][C] 25.1[/C][/ROW]
[ROW][C]43[/C][C] 1529[/C][C] 1408[/C][C] 121.1[/C][/ROW]
[ROW][C]44[/C][C] 1366[/C][C] 1281[/C][C] 85.32[/C][/ROW]
[ROW][C]45[/C][C] 1357[/C][C] 1217[/C][C] 139.5[/C][/ROW]
[ROW][C]46[/C][C] 1570[/C][C] 1519[/C][C] 50.96[/C][/ROW]
[ROW][C]47[/C][C] 1535[/C][C] 1754[/C][C]-219.2[/C][/ROW]
[ROW][C]48[/C][C] 2491[/C][C] 2452[/C][C] 39.29[/C][/ROW]
[ROW][C]49[/C][C] 3084[/C][C] 2862[/C][C] 222.1[/C][/ROW]
[ROW][C]50[/C][C] 2605[/C][C] 2714[/C][C]-108.8[/C][/ROW]
[ROW][C]51[/C][C] 2573[/C][C] 2569[/C][C] 3.669[/C][/ROW]
[ROW][C]52[/C][C] 2143[/C][C] 2241[/C][C]-98.02[/C][/ROW]
[ROW][C]53[/C][C] 1693[/C][C] 1609[/C][C] 83.76[/C][/ROW]
[ROW][C]54[/C][C] 1504[/C][C] 1448[/C][C] 55.52[/C][/ROW]
[ROW][C]55[/C][C] 1461[/C][C] 1365[/C][C] 96.24[/C][/ROW]
[ROW][C]56[/C][C] 1354[/C][C] 1219[/C][C] 134.6[/C][/ROW]
[ROW][C]57[/C][C] 1333[/C][C] 1174[/C][C] 159.4[/C][/ROW]
[ROW][C]58[/C][C] 1492[/C][C] 1459[/C][C] 32.54[/C][/ROW]
[ROW][C]59[/C][C] 1781[/C][C] 1759[/C][C] 21.67[/C][/ROW]
[ROW][C]60[/C][C] 1915[/C][C] 2277[/C][C]-361.7[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318153&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318153&T=5

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	2933	2974	-41.36
2	2889	3276	-387.1
3	2938	2924	13.95
4	2497	2327	169.9
5	1870	1898	-27.87
6	1726	1757	-31.18
7	1607	1710	-103
8	1545	1554	-9.376
9	1396	1522	-126.2
10	1787	1598	188.5
11	2076	1885	191.3
12	2837	2634	202.6
13	2787	3041	-253.8
14	3891	3035	855.6
15	3179	2942	237.5
16	2011	2059	-47.58
17	1636	1711	-75.36
18	1580	1640	-60.46
19	1489	1492	-2.853
20	1300	1414	-114.2
21	1356	1417	-60.91
22	1653	1817	-164.1
23	2013	1868	145.2
24	2823	2722	100.6
25	3102	3097	4.625
26	2294	2625	-330.8
27	2385	2473	-88.43
28	2444	2370	74.35
29	1748	1870	-122.4
30	1554	1543	11.03
31	1498	1609	-111.4
32	1361	1457	-96.27
33	1346	1458	-111.8
34	1564	1672	-107.9
35	1640	1779	-139
36	2293	2274	19.24
37	2815	2747	68.39
38	3137	3166	-28.92
39	2679	2846	-166.7
40	1969	2068	-98.63
41	1870	1728	141.9
42	1633	1608	25.1
43	1529	1408	121.1
44	1366	1281	85.32
45	1357	1217	139.5
46	1570	1519	50.96
47	1535	1754	-219.2
48	2491	2452	39.29
49	3084	2862	222.1
50	2605	2714	-108.8
51	2573	2569	3.669
52	2143	2241	-98.02
53	1693	1609	83.76
54	1504	1448	55.52
55	1461	1365	96.24
56	1354	1219	134.6
57	1333	1174	159.4
58	1492	1459	32.54
59	1781	1759	21.67
60	1915	2277	-361.7

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
28	0.9996	0.0007376	0.0003688
29	0.9982	0.003624	0.001812
30	0.9919	0.01619	0.008093
31	0.9718	0.05633	0.02817
32	0.9135	0.1731	0.08655

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
28 &  0.9996 &  0.0007376 &  0.0003688 \tabularnewline
29 &  0.9982 &  0.003624 &  0.001812 \tabularnewline
30 &  0.9919 &  0.01619 &  0.008093 \tabularnewline
31 &  0.9718 &  0.05633 &  0.02817 \tabularnewline
32 &  0.9135 &  0.1731 &  0.08655 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318153&T=6

[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]28[/C][C] 0.9996[/C][C] 0.0007376[/C][C] 0.0003688[/C][/ROW]
[ROW][C]29[/C][C] 0.9982[/C][C] 0.003624[/C][C] 0.001812[/C][/ROW]
[ROW][C]30[/C][C] 0.9919[/C][C] 0.01619[/C][C] 0.008093[/C][/ROW]
[ROW][C]31[/C][C] 0.9718[/C][C] 0.05633[/C][C] 0.02817[/C][/ROW]
[ROW][C]32[/C][C] 0.9135[/C][C] 0.1731[/C][C] 0.08655[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318153&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318153&T=6

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
28	0.9996	0.0007376	0.0003688
29	0.9982	0.003624	0.001812
30	0.9919	0.01619	0.008093
31	0.9718	0.05633	0.02817
32	0.9135	0.1731	0.08655

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	2	0.4	NOK
5% type I error level	3	0.6	NOK
10% type I error level	4	0.8	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 & 2 &  0.4 & NOK \tabularnewline
5% type I error level & 3 & 0.6 & NOK \tabularnewline
10% type I error level & 4 & 0.8 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318153&T=7

[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]2[/C][C] 0.4[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]3[/C][C]0.6[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.8[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318153&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318153&T=7

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	2	0.4	NOK
5% type I error level	3	0.6	NOK
10% type I error level	4	0.8	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 7.0456, df1 = 2, df2 = 33, p-value = 0.002831
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = -0.43364, df1 = 48, df2 = -13, p-value = NA
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.40428, df1 = 2, df2 = 33, p-value = 0.6707

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 7.0456, df1 = 2, df2 = 33, p-value = 0.002831
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = -0.43364, df1 = 48, df2 = -13, p-value = NA
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.40428, df1 = 2, df2 = 33, p-value = 0.6707
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318153&T=8

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 7.0456, df1 = 2, df2 = 33, p-value = 0.002831
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = -0.43364, df1 = 48, df2 = -13, p-value = NA
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.40428, df1 = 2, df2 = 33, p-value = 0.6707
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318153&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318153&T=8

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 7.0456, df1 = 2, df2 = 33, p-value = 0.002831
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = -0.43364, df1 = 48, df2 = -13, p-value = NA
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.40428, df1 = 2, df2 = 33, p-value = 0.6707

Variance Inflation Factors (Multicollinearity)

> vif
 `F1C(t-1)`  `F1C(t-2)`  `F1C(t-3)`  `F1C(t-4)`  `F1C(t-5)`  `F1C(t-6)` 
  12.518333   12.453451   12.529724   12.522414   12.521569   12.442948 
 `F1C(t-7)`  `F1C(t-8)`  `F1C(t-9)` `F1C(t-10)` `F1C(t-11)` `F1C(t-12)` 
  12.321787   12.285902   12.411126   12.471964   10.935649   10.845821 
         M1          M2          M3          M4          M5          M6 
   5.266731   12.433374   22.635058   31.390465   34.680240   33.091501 
         M7          M8          M9         M10         M11           t 
  29.015216   23.490252   16.138635    8.600256    3.852449    6.014358

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
 `F1C(t-1)`  `F1C(t-2)`  `F1C(t-3)`  `F1C(t-4)`  `F1C(t-5)`  `F1C(t-6)` 
  12.518333   12.453451   12.529724   12.522414   12.521569   12.442948 
 `F1C(t-7)`  `F1C(t-8)`  `F1C(t-9)` `F1C(t-10)` `F1C(t-11)` `F1C(t-12)` 
  12.321787   12.285902   12.411126   12.471964   10.935649   10.845821 
         M1          M2          M3          M4          M5          M6 
   5.266731   12.433374   22.635058   31.390465   34.680240   33.091501 
         M7          M8          M9         M10         M11           t 
  29.015216   23.490252   16.138635    8.600256    3.852449    6.014358 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318153&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
 `F1C(t-1)`  `F1C(t-2)`  `F1C(t-3)`  `F1C(t-4)`  `F1C(t-5)`  `F1C(t-6)` 
  12.518333   12.453451   12.529724   12.522414   12.521569   12.442948 
 `F1C(t-7)`  `F1C(t-8)`  `F1C(t-9)` `F1C(t-10)` `F1C(t-11)` `F1C(t-12)` 
  12.321787   12.285902   12.411126   12.471964   10.935649   10.845821 
         M1          M2          M3          M4          M5          M6 
   5.266731   12.433374   22.635058   31.390465   34.680240   33.091501 
         M7          M8          M9         M10         M11           t 
  29.015216   23.490252   16.138635    8.600256    3.852449    6.014358 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318153&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318153&T=9

As an alternative you can also use a QR Code:

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

Variance Inflation Factors (Multicollinearity)

> vif
 `F1C(t-1)`  `F1C(t-2)`  `F1C(t-3)`  `F1C(t-4)`  `F1C(t-5)`  `F1C(t-6)` 
  12.518333   12.453451   12.529724   12.522414   12.521569   12.442948 
 `F1C(t-7)`  `F1C(t-8)`  `F1C(t-9)` `F1C(t-10)` `F1C(t-11)` `F1C(t-12)` 
  12.321787   12.285902   12.411126   12.471964   10.935649   10.845821 
         M1          M2          M3          M4          M5          M6 
   5.266731   12.433374   22.635058   31.390465   34.680240   33.091501 
         M7          M8          M9         M10         M11           t 
  29.015216   23.490252   16.138635    8.600256    3.852449    6.014358

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 12 ; par2 = Triple ; par3 = additive ; par4 = 12 ;

Parameters (R input):

par1 = ; par2 = Include Seasonal Dummies ; par3 = Linear Trend ; par4 = 12 ; par5 = ; par6 = 12 ;

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

par6 <- '12'
par5 <- ''
par4 <- '12'
par3 <- 'No Linear Trend'
par2 <- 'Include Seasonal Dummies'
par1 <- ''
library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par6 <- as.numeric(par6)
if(is.na(par6)) {
par6 <- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (!is.numeric(par4)) par4 <- 0
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
if (!is.numeric(par5)) par5 <- 0
x <- na.omit(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'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s)'){
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*par6)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*par6-j*par6,par1]
}
}
x <- cbind(x[(par5*par6+1):n,], x2)
n <- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
for (i in 1:(par6-1)){
x2[seq(i,n,par6),i] <- 1
}
x <- cbind(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[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
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,'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$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
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,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
a <-table.start()
a <- table.row.start(a)
a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Description',1,TRUE)
a <- table.element(a,'Link',1,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Histogram',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'QQ Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Spectral Analysis',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Summary Statistics',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code