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Author's title

Author

*Unverified author*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sat, 04 Jan 2020 14:16:19 +0100

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2020/Jan/04/t157814801772wk8zu0erslp20.htm/, Retrieved Sat, 20 Apr 2024 11:09:07 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=318984, Retrieved Sat, 20 Apr 2024 11:09:07 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Multiple Regression] [drug interactions 2] [2020-01-04 13:16:19] [d41d8cd98f00b204e9800998ecf8427e] [Current]

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0	0	0	1	0	0	0	0	0	0	0	1	1	0	0	0	0	0	3	5
0	0	0	1	0	1	1	0	0	1	0	0	1	0	0	1	0	1	0	6
0	0	0	1	0	0	1	1	0	1	0	1	0	0	0	0	0	0	0	3
0	0	0	0	0	2	0	1	0	0	0	0	1	0	0	0	0	1	0	7
0	0	0	1	1	0	1	0	0	0	1	0	0	0	1	1	0	0	0	6
0	0	1	0	0	0	0	1	0	1	0	1	1	0	1	0	0	0	0	3
0	0	1	0	0	1	0	1	0	2	1	0	0	0	0	1	0	0	0	8
0	0	1	0	1	1	0	0	0	0	0	0	1	0	0	0	0	1	2	7
0	0	1	0	0	0	1	0	0	1	0	0	0	1	1	0	0	0	0	7
0	0	1	0	0	1	0	0	0	0	2	0	0	0	1	0	0	0	0	8
1	0	1	0	1	1	1	0	0	0	0	0	0	0	0	0	0	1	0	2
0	0	1	1	1	0	0	1	0	0	0	0	0	0	1	0	0	0	0	4
0	0	0	1	1	1	1	0	0	0	0	0	1	0	0	1	0	0	1	8
0	0	0	1	1	1	0	0	0	0	0	0	0	0	1	1	0	0	0	7
0	0	0	1	0	2	0	1	0	0	0	0	0	0	1	0	0	0	0	4
0	0	1	0	0	2	0	1	0	0	0	0	0	0	0	0	0	0	0	4
0	0	0	1	1	2	0	0	0	0	0	0	0	0	0	0	0	0	0	1
0	0	0	0	1	0	1	1	0	0	0	0	0	0	0	1	0	0	0	5
0	0	0	1	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	4
0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	3
0	0	0	1	1	0	2	0	0	0	1	0	0	0	1	1	0	0	0	5
0	0	0	2	0	2	1	0	0	0	0	0	0	0	1	1	0	0	0	6
1	0	0	1	1	0	0	1	0	0	0	0	0	0	1	1	0	0	0	5
1	0	0	1	1	1	0	0	0	0	0	0	0	0	1	0	0	0	0	4
1	0	0	1	0	0	1	1	0	2	0	0	0	0	0	1	0	0	0	1
0	0	1	0	0	0	0	0	1	2	1	0	0	0	0	0	0	0	0	5
1	0	1	0	0	0	0	1	0	2	0	0	0	0	0	1	0	0	0	2
0	0	1	0	1	2	0	1	0	0	0	0	1	1	0	0	0	0	0	8
0	1	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	7
0	0	1	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	0	4
0	0	0	0	1	1	0	0	1	2	0	1	1	0	0	0	0	0	0	13
0	0	0	1	0	1	0	0	0	1	0	1	1	0	0	0	0	1	1	7
1	0	1	0	1	1	0	0	0	2	0	0	0	0	0	0	0	0	0	8
1	0	1	0	0	1	1	1	0	0	0	0	0	1	1	0	0	1	0	9
0	0	0	1	0	1	1	1	0	1	0	0	0	0	0	0	0	0	0	8
0	0	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	7
0	0	0	1	0	1	1	1	0	1	0	0	1	0	1	1	0	0	0	5
0	0	0	1	0	0	0	0	0	0	0	0	2	0	1	1	0	0	1	6
0	0	1	0	0	2	0	1	0	1	0	0	0	0	1	1	0	0	0	7
1	0	1	0	0	1	0	1	0	0	0	0	0	0	1	1	0	0	0	9
0	0	0	2	0	0	0	1	0	1	0	0	0	0	0	0	0	2	0	2
0	0	0	4	0	0	0	0	0	0	0	0	1	1	1	1	0	0	1	6
0	0	0	1	1	1	0	1	0	0	0	1	1	0	0	1	0	1	0	11
0	0	1	2	0	0	0	1	0	0	1	0	0	0	1	1	0	0	0	10
0	1	1	1	1	1	0	0	0	0	0	0	0	0	1	1	0	0	0	11
1	0	0	1	1	2	0	1	0	0	0	0	0	0	1	0	0	0	0	7
0	0	1	0	1	2	0	1	1	1	0	1	0	0	0	0	0	0	0	8
0	0	1	0	0	2	1	0	0	0	0	0	1	0	0	0	0	0	0	6
0	0	0	1	1	1	0	0	0	0	0	0	0	0	1	1	0	0	2	7
0	0	0	1	1	1	0	0	0	1	0	1	0	1	0	1	0	0	0	8
0	0	0	1	1	0	0	1	0	2	0	0	0	0	1	1	0	0	0	6
1	1	1	0	0	1	1	0	0	0	0	0	0	0	1	1	0	0	0	8
0	0	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	9
0	0	0	1	1	1	1	0	0	0	2	0	0	0	0	1	0	0	0	10
0	0	0	0	0	3	0	1	0	0	1	0	0	0	0	1	0	0	0	9
0	0	0	2	1	1	1	1	0	0	1	0	0	0	0	2	0	0	0	13
0	0	0	1	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	1
0	1	1	0	1	2	0	1	0	1	1	0	0	0	0	1	0	0	0	14
0	0	0	1	0	0	0	1	1	2	0	0	0	1	0	1	0	0	0	12
1	0	1	1	1	0	0	1	0	0	0	0	1	0	1	2	0	0	0	9
0	0	1	0	0	0	0	1	0	0	0	0	1	0	1	0	0	1	1	5
0	0	0	2	0	2	0	1	1	2	0	0	0	0	0	1	0	0	0	11
1	0	1	0	0	1	0	1	0	2	0	0	0	1	1	0	0	0	0	12
0	0	1	0	1	1	0	1	0	0	0	0	0	0	1	0	0	0	0	9
0	0	1	0	1	0	1	0	0	2	0	0	0	0	0	0	0	0	0	11
1	0	1	0	1	2	0	1	1	0	0	0	0	0	1	1	0	0	0	10
0	0	0	1	1	2	0	0	0	0	0	0	0	0	0	0	0	0	0	6
0	0	0	1	0	2	0	1	0	1	1	0	0	0	0	1	0	0	0	15
0	0	0	0	0	0	0	1	0	0	0	0	1	2	1	1	0	1	0	13
1	0	1	1	0	1	0	0	1	0	0	1	0	0	1	0	0	0	0	4
0	0	0	0	0	0	0	1	0	0	0	0	0	1	1	0	0	0	2	11
0	0	0	2	0	0	1	1	1	1	0	0	0	0	1	1	0	0	0	11
0	0	0	1	0	1	0	1	0	0	0	0	0	1	1	0	0	1	0	10
0	0	0	1	1	0	1	0	0	0	0	0	1	1	1	0	0	1	1	12
1	0	1	0	0	2	0	1	1	0	1	1	0	0	1	0	0	0	0	13
1	0	0	1	1	2	0	1	0	0	0	0	0	0	1	0	0	0	0	4
0	0	0	1	1	0	0	1	0	0	0	0	0	0	1	1	0	0	0	5
0	0	0	1	1	2	0	0	0	0	0	0	1	1	0	1	0	0	0	10
0	1	1	0	1	0	1	1	0	0	0	0	0	0	0	0	0	0	0	4
0	0	0	0	1	0	0	0	0	2	1	0	1	0	0	0	0	1	0	8
0	0	1	0	1	1	0	1	0	1	0	1	0	0	0	0	0	0	0	15
1	0	1	0	1	1	0	1	0	1	1	0	0	0	1	0	0	0	0	7
1	0	0	1	0	1	0	1	0	0	1	0	0	0	1	1	0	0	0	6
1	0	1	0	1	0	1	1	0	2	0	0	0	0	0	1	0	0	0	4
0	0	0	1	1	2	0	1	0	0	1	0	0	0	1	1	0	0	0	12
0	0	0	1	0	1	1	0	0	0	0	0	0	1	0	0	0	1	1	12
0	0	0	1	1	0	1	1	0	0	0	0	1	2	0	0	0	1	0	14
0	0	0	1	1	0	1	1	1	2	1	0	0	0	1	1	0	0	0	16
1	0	0	1	0	1	1	1	0	0	0	1	0	0	0	1	0	0	0	6
0	0	0	1	1	1	0	1	0	0	0	0	1	1	1	1	0	1	0	12
0	0	0	1	1	2	1	1	0	0	0	1	0	0	0	0	0	0	0	22
0	0	0	1	0	0	0	1	0	1	0	1	1	0	0	0	0	1	2	9
1	0	0	2	0	1	0	1	0	0	1	0	0	0	0	1	0	0	1	10
1	0	1	0	0	2	0	0	0	0	1	1	1	0	1	1	0	0	0	10
0	0	1	1	1	1	1	0	0	0	0	0	0	0	1	0	0	0	0	14
0	0	0	1	1	1	0	0	1	2	0	0	0	0	0	2	0	0	0	9
1	0	1	0	1	2	0	1	1	2	0	0	0	0	0	0	0	0	0	15
1	0	0	1	1	2	1	0	0	1	1	0	0	0	1	0	0	0	0	14
0	0	1	0	1	2	0	1	0	1	0	0	0	0	0	1	0	0	0	12
1	1	0	1	0	2	1	0	0	0	0	0	0	0	0	2	0	1	0	9
0	0	1	0	1	1	1	0	0	1	1	0	1	0	1	0	0	0	1	10
0	0	0	1	1	1	1	1	0	0	1	0	1	0	1	1	0	1	0	13
1	0	0	1	2	1	1	0	1	0	0	1	1	0	1	1	0	0	0	17
0	0	1	0	1	2	0	1	0	2	0	0	0	0	1	1	0	0	0	16
0	0	0	1	1	0	1	1	0	1	1	1	1	1	0	1	0	0	0	17
0	0	0	0	1	1	0	1	0	0	0	0	1	1	1	1	0	1	1	21
0	0	0	1	0	1	1	1	0	1	0	0	1	0	1	0	2	0	0	11
0	0	1	1	0	0	0	0	0	0	1	1	2	0	1	2	1	0	0	22
0	0	1	0	1	1	1	0	0	0	1	1	1	0	1	1	0	0	0	14
0	0	0	0	1	2	0	0	0	0	1	1	1	0	1	1	0	0	0	12
0	0	1	0	1	3	0	1	0	1	0	0	0	1	0	1	0	1	0	13
0	1	1	1	1	2	0	1	0	0	1	0	0	0	1	1	0	0	0	19
1	0	1	0	1	1	0	1	0	0	0	0	2	0	1	0	0	0	0	15
1	0	1	0	1	1	0	1	0	0	1	2	1	2	0	0	0	0	0	7
0	1	0	1	1	1	0	1	0	0	0	1	2	2	1	2	0	1	0	43

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 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 time4 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318984&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]

4 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=318984&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318984&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	4 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
t[t] = -3.57268 -0.186077a[t] + 5.40399b[t] + 1.06096c[t] + 1.12635d[t] + 1.88479e[t] + 2.06006f[t] + 1.42984g[t] + 2.52023h[t] + 2.19534i[t] + 1.33772j[t] + 2.35474k[t] + 1.99608l[t] + 2.58153m[t] + 2.49687n[t] + 1.44277o[t] + 1.38921p[t] + 2.14259q[t] + 1.26339r[t] + 1.65038s[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
t[t] =  -3.57268 -0.186077a[t] +  5.40399b[t] +  1.06096c[t] +  1.12635d[t] +  1.88479e[t] +  2.06006f[t] +  1.42984g[t] +  2.52023h[t] +  2.19534i[t] +  1.33772j[t] +  2.35474k[t] +  1.99608l[t] +  2.58153m[t] +  2.49687n[t] +  1.44277o[t] +  1.38921p[t] +  2.14259q[t] +  1.26339r[t] +  1.65038s[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318984&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]t[t] =  -3.57268 -0.186077a[t] +  5.40399b[t] +  1.06096c[t] +  1.12635d[t] +  1.88479e[t] +  2.06006f[t] +  1.42984g[t] +  2.52023h[t] +  2.19534i[t] +  1.33772j[t] +  2.35474k[t] +  1.99608l[t] +  2.58153m[t] +  2.49687n[t] +  1.44277o[t] +  1.38921p[t] +  2.14259q[t] +  1.26339r[t] +  1.65038s[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318984&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318984&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[t] = -3.57268 -0.186077a[t] + 5.40399b[t] + 1.06096c[t] + 1.12635d[t] + 1.88479e[t] + 2.06006f[t] + 1.42984g[t] + 2.52023h[t] + 2.19534i[t] + 1.33772j[t] + 2.35474k[t] + 1.99608l[t] + 2.58153m[t] + 2.49687n[t] + 1.44277o[t] + 1.38921p[t] + 2.14259q[t] + 1.26339r[t] + 1.65038s[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	-3.573	0.4019	-8.8890e+00	7.187e-17	3.594e-17
a	-0.1861	0.4705	-3.9550e-01	0.6928	0.3464
b	+5.404	0.9593	+5.6330e+00	4.243e-08	2.122e-08
c	+1.061	0.4079	+2.6010e+00	0.009777	0.004889
d	+1.126	0.316	+3.5640e+00	0.0004275	0.0002138
e	+1.885	0.3307	+5.6990e+00	3.011e-08	1.505e-08
f	+2.06	0.2353	+8.7540e+00	1.849e-16	9.247e-17
g	+1.43	0.3809	+3.7540e+00	0.0002109	0.0001055
h	+2.52	0.3387	+7.4400e+00	1.194e-12	5.969e-13
i	+2.195	0.7008	+3.1320e+00	0.001914	0.0009568
j	+1.338	0.2778	+4.8150e+00	2.397e-06	1.198e-06
k	+2.355	0.4828	+4.8770e+00	1.793e-06	8.967e-07
l	+1.996	0.4799	+4.1590e+00	4.227e-05	2.113e-05
m	+2.582	0.4167	+6.1950e+00	2.031e-09	1.015e-09
n	+2.497	0.3962	+6.3020e+00	1.114e-09	5.57e-10
o	+1.443	0.355	+4.0640e+00	6.236e-05	3.118e-05
p	+1.389	0.3331	+4.1710e+00	4.029e-05	2.015e-05
q	+2.143	0.8449	+2.5360e+00	0.01175	0.005875
r	+1.263	0.4679	+2.7000e+00	0.007349	0.003674
s	+1.65	0.4517	+3.6540e+00	0.0003073	0.0001537

\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) & -3.573 &  0.4019 & -8.8890e+00 &  7.187e-17 &  3.594e-17 \tabularnewline
a & -0.1861 &  0.4705 & -3.9550e-01 &  0.6928 &  0.3464 \tabularnewline
b & +5.404 &  0.9593 & +5.6330e+00 &  4.243e-08 &  2.122e-08 \tabularnewline
c & +1.061 &  0.4079 & +2.6010e+00 &  0.009777 &  0.004889 \tabularnewline
d & +1.126 &  0.316 & +3.5640e+00 &  0.0004275 &  0.0002138 \tabularnewline
e & +1.885 &  0.3307 & +5.6990e+00 &  3.011e-08 &  1.505e-08 \tabularnewline
f & +2.06 &  0.2353 & +8.7540e+00 &  1.849e-16 &  9.247e-17 \tabularnewline
g & +1.43 &  0.3809 & +3.7540e+00 &  0.0002109 &  0.0001055 \tabularnewline
h & +2.52 &  0.3387 & +7.4400e+00 &  1.194e-12 &  5.969e-13 \tabularnewline
i & +2.195 &  0.7008 & +3.1320e+00 &  0.001914 &  0.0009568 \tabularnewline
j & +1.338 &  0.2778 & +4.8150e+00 &  2.397e-06 &  1.198e-06 \tabularnewline
k & +2.355 &  0.4828 & +4.8770e+00 &  1.793e-06 &  8.967e-07 \tabularnewline
l & +1.996 &  0.4799 & +4.1590e+00 &  4.227e-05 &  2.113e-05 \tabularnewline
m & +2.582 &  0.4167 & +6.1950e+00 &  2.031e-09 &  1.015e-09 \tabularnewline
n & +2.497 &  0.3962 & +6.3020e+00 &  1.114e-09 &  5.57e-10 \tabularnewline
o & +1.443 &  0.355 & +4.0640e+00 &  6.236e-05 &  3.118e-05 \tabularnewline
p & +1.389 &  0.3331 & +4.1710e+00 &  4.029e-05 &  2.015e-05 \tabularnewline
q & +2.143 &  0.8449 & +2.5360e+00 &  0.01175 &  0.005875 \tabularnewline
r & +1.263 &  0.4679 & +2.7000e+00 &  0.007349 &  0.003674 \tabularnewline
s & +1.65 &  0.4517 & +3.6540e+00 &  0.0003073 &  0.0001537 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318984&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]-3.573[/C][C] 0.4019[/C][C]-8.8890e+00[/C][C] 7.187e-17[/C][C] 3.594e-17[/C][/ROW]
[ROW][C]a[/C][C]-0.1861[/C][C] 0.4705[/C][C]-3.9550e-01[/C][C] 0.6928[/C][C] 0.3464[/C][/ROW]
[ROW][C]b[/C][C]+5.404[/C][C] 0.9593[/C][C]+5.6330e+00[/C][C] 4.243e-08[/C][C] 2.122e-08[/C][/ROW]
[ROW][C]c[/C][C]+1.061[/C][C] 0.4079[/C][C]+2.6010e+00[/C][C] 0.009777[/C][C] 0.004889[/C][/ROW]
[ROW][C]d[/C][C]+1.126[/C][C] 0.316[/C][C]+3.5640e+00[/C][C] 0.0004275[/C][C] 0.0002138[/C][/ROW]
[ROW][C]e[/C][C]+1.885[/C][C] 0.3307[/C][C]+5.6990e+00[/C][C] 3.011e-08[/C][C] 1.505e-08[/C][/ROW]
[ROW][C]f[/C][C]+2.06[/C][C] 0.2353[/C][C]+8.7540e+00[/C][C] 1.849e-16[/C][C] 9.247e-17[/C][/ROW]
[ROW][C]g[/C][C]+1.43[/C][C] 0.3809[/C][C]+3.7540e+00[/C][C] 0.0002109[/C][C] 0.0001055[/C][/ROW]
[ROW][C]h[/C][C]+2.52[/C][C] 0.3387[/C][C]+7.4400e+00[/C][C] 1.194e-12[/C][C] 5.969e-13[/C][/ROW]
[ROW][C]i[/C][C]+2.195[/C][C] 0.7008[/C][C]+3.1320e+00[/C][C] 0.001914[/C][C] 0.0009568[/C][/ROW]
[ROW][C]j[/C][C]+1.338[/C][C] 0.2778[/C][C]+4.8150e+00[/C][C] 2.397e-06[/C][C] 1.198e-06[/C][/ROW]
[ROW][C]k[/C][C]+2.355[/C][C] 0.4828[/C][C]+4.8770e+00[/C][C] 1.793e-06[/C][C] 8.967e-07[/C][/ROW]
[ROW][C]l[/C][C]+1.996[/C][C] 0.4799[/C][C]+4.1590e+00[/C][C] 4.227e-05[/C][C] 2.113e-05[/C][/ROW]
[ROW][C]m[/C][C]+2.582[/C][C] 0.4167[/C][C]+6.1950e+00[/C][C] 2.031e-09[/C][C] 1.015e-09[/C][/ROW]
[ROW][C]n[/C][C]+2.497[/C][C] 0.3962[/C][C]+6.3020e+00[/C][C] 1.114e-09[/C][C] 5.57e-10[/C][/ROW]
[ROW][C]o[/C][C]+1.443[/C][C] 0.355[/C][C]+4.0640e+00[/C][C] 6.236e-05[/C][C] 3.118e-05[/C][/ROW]
[ROW][C]p[/C][C]+1.389[/C][C] 0.3331[/C][C]+4.1710e+00[/C][C] 4.029e-05[/C][C] 2.015e-05[/C][/ROW]
[ROW][C]q[/C][C]+2.143[/C][C] 0.8449[/C][C]+2.5360e+00[/C][C] 0.01175[/C][C] 0.005875[/C][/ROW]
[ROW][C]r[/C][C]+1.263[/C][C] 0.4679[/C][C]+2.7000e+00[/C][C] 0.007349[/C][C] 0.003674[/C][/ROW]
[ROW][C]s[/C][C]+1.65[/C][C] 0.4517[/C][C]+3.6540e+00[/C][C] 0.0003073[/C][C] 0.0001537[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318984&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318984&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)	-3.573	0.4019	-8.8890e+00	7.187e-17	3.594e-17
a	-0.1861	0.4705	-3.9550e-01	0.6928	0.3464
b	+5.404	0.9593	+5.6330e+00	4.243e-08	2.122e-08
c	+1.061	0.4079	+2.6010e+00	0.009777	0.004889
d	+1.126	0.316	+3.5640e+00	0.0004275	0.0002138
e	+1.885	0.3307	+5.6990e+00	3.011e-08	1.505e-08
f	+2.06	0.2353	+8.7540e+00	1.849e-16	9.247e-17
g	+1.43	0.3809	+3.7540e+00	0.0002109	0.0001055
h	+2.52	0.3387	+7.4400e+00	1.194e-12	5.969e-13
i	+2.195	0.7008	+3.1320e+00	0.001914	0.0009568
j	+1.338	0.2778	+4.8150e+00	2.397e-06	1.198e-06
k	+2.355	0.4828	+4.8770e+00	1.793e-06	8.967e-07
l	+1.996	0.4799	+4.1590e+00	4.227e-05	2.113e-05
m	+2.582	0.4167	+6.1950e+00	2.031e-09	1.015e-09
n	+2.497	0.3962	+6.3020e+00	1.114e-09	5.57e-10
o	+1.443	0.355	+4.0640e+00	6.236e-05	3.118e-05
p	+1.389	0.3331	+4.1710e+00	4.029e-05	2.015e-05
q	+2.143	0.8449	+2.5360e+00	0.01175	0.005875
r	+1.263	0.4679	+2.7000e+00	0.007349	0.003674
s	+1.65	0.4517	+3.6540e+00	0.0003073	0.0001537

Multiple Linear Regression - Regression Statistics
Multiple R	0.8508
R-squared	0.7239
Adjusted R-squared	0.7054
F-TEST (value)	39.32
F-TEST (DF numerator)	19
F-TEST (DF denominator)	285
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.821
Sum Squared Residuals	2268

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.8508 \tabularnewline
R-squared &  0.7239 \tabularnewline
Adjusted R-squared &  0.7054 \tabularnewline
F-TEST (value) &  39.32 \tabularnewline
F-TEST (DF numerator) & 19 \tabularnewline
F-TEST (DF denominator) & 285 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.821 \tabularnewline
Sum Squared Residuals &  2268 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318984&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.8508[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.7239[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.7054[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 39.32[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]19[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]285[/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] 2.821[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 2268[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318984&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318984&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	0.8508
R-squared	0.7239
Adjusted R-squared	0.7054
F-TEST (value)	39.32
F-TEST (DF numerator)	19
F-TEST (DF denominator)	285
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.821
Sum Squared Residuals	2268

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=318984&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=318984&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318984&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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 30.113, df1 = 2, df2 = 283, p-value = 1.39e-12
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.2343, df1 = 38, df2 = 247, p-value = 0.1751
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.10934, df1 = 2, df2 = 283, p-value = 0.8965

\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 = 30.113, df1 = 2, df2 = 283, p-value = 1.39e-12
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.2343, df1 = 38, df2 = 247, p-value = 0.1751
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.10934, df1 = 2, df2 = 283, p-value = 0.8965
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318984&T=5

[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 = 30.113, df1 = 2, df2 = 283, p-value = 1.39e-12
[/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 = 1.2343, df1 = 38, df2 = 247, p-value = 0.1751
[/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 = 0.10934, df1 = 2, df2 = 283, p-value = 0.8965
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318984&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318984&T=5

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 = 30.113, df1 = 2, df2 = 283, p-value = 1.39e-12
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.2343, df1 = 38, df2 = 247, p-value = 0.1751
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.10934, df1 = 2, df2 = 283, p-value = 0.8965

Variance Inflation Factors (Multicollinearity)

> vif
       a        b        c        d        e        f        g        h 
1.086576 1.118627 1.334997 1.324731 1.154964 1.202919 1.067610 1.108588 
       i        j        k        l        m        n        o        p 
1.312628 1.337731 1.113680 1.205507 1.434102 1.166536 1.162362 1.308818 
       q        r        s 
1.057709 1.204044 1.172960

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
       a        b        c        d        e        f        g        h 
1.086576 1.118627 1.334997 1.324731 1.154964 1.202919 1.067610 1.108588 
       i        j        k        l        m        n        o        p 
1.312628 1.337731 1.113680 1.205507 1.434102 1.166536 1.162362 1.308818 
       q        r        s 
1.057709 1.204044 1.172960 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318984&T=6

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
       a        b        c        d        e        f        g        h 
1.086576 1.118627 1.334997 1.324731 1.154964 1.202919 1.067610 1.108588 
       i        j        k        l        m        n        o        p 
1.312628 1.337731 1.113680 1.205507 1.434102 1.166536 1.162362 1.308818 
       q        r        s 
1.057709 1.204044 1.172960 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318984&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318984&T=6

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
       a        b        c        d        e        f        g        h 
1.086576 1.118627 1.334997 1.324731 1.154964 1.202919 1.067610 1.108588 
       i        j        k        l        m        n        o        p 
1.312628 1.337731 1.113680 1.205507 1.434102 1.166536 1.162362 1.308818 
       q        r        s 
1.057709 1.204044 1.172960

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 5

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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 = 20 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par6 = 12 ;

Parameters (R input):

par1 = 20 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ; par6 = 12 ;

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

par6 <- '12'
par5 <- ''
par4 <- ''
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '21'
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