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*Unverified author*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sat, 04 Jan 2020 12:18:05 +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/t1578137772x9huaj3dxl4z1yb.htm/, Retrieved Fri, 19 Apr 2024 10:45:18 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Fri, 19 Apr 2024 10:45:18 +0200

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7	0	0	0	1	1	1	0	1	0	3	0	0	0	0	0	0	0	0	0	7
7	0	0	0	1	1	2	0	0	0	0	0	0	0	0	1	0	0	0	0	3
7	0	0	0	0	0	0	1	0	0	0	1	1	0	1	1	0	0	1	0	7
7	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	1	1
7	0	0	1	0	1	1	1	1	0	0	0	0	0	0	1	0	0	0	0	7
7	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	1
7	1	0	0	1	0	2	0	1	0	1	0	0	0	0	0	0	0	0	0	3
7	0	0	0	1	0	0	0	0	0	0	0	1	1	0	0	0	0	0	3	5
7	0	0	0	1	0	1	1	0	0	1	0	0	1	0	0	1	0	1	0	6
7	0	0	0	1	0	0	1	1	0	1	0	1	0	0	0	0	0	0	0	3
7	0	0	0	0	0	2	0	1	0	0	0	0	1	0	0	0	0	1	0	7
7	0	0	0	1	1	0	1	0	0	0	1	0	0	0	1	1	0	0	0	6
7	0	0	1	0	0	0	0	1	0	1	0	1	1	0	1	0	0	0	0	3
7	0	0	1	0	0	1	0	1	0	2	1	0	0	0	0	1	0	0	0	8
7	0	0	1	0	1	1	0	0	0	0	0	0	1	0	0	0	0	1	2	7
7	0	0	1	0	0	0	1	0	0	1	0	0	0	1	1	0	0	0	0	7
7	0	0	1	0	0	1	0	0	0	0	2	0	0	0	1	0	0	0	0	8
7	1	0	1	0	1	1	1	0	0	0	0	0	0	0	0	0	0	1	0	2
7	0	0	1	1	1	0	0	1	0	0	0	0	0	0	1	0	0	0	0	4
7	0	0	0	1	1	1	1	0	0	0	0	0	1	0	0	1	0	0	1	8
7	0	0	0	1	1	1	0	0	0	0	0	0	0	0	1	1	0	0	0	7
7	0	0	0	1	0	2	0	1	0	0	0	0	0	0	1	0	0	0	0	4
7	0	0	1	0	0	2	0	1	0	0	0	0	0	0	0	0	0	0	0	4
7	0	0	0	1	1	2	0	0	0	0	0	0	0	0	0	0	0	0	0	1
7	0	0	0	0	1	0	1	1	0	0	0	0	0	0	0	1	0	0	0	5
7	0	0	0	1	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	4
7	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	3
7	0	0	0	1	1	0	2	0	0	0	1	0	0	0	1	1	0	0	0	5
7	0	0	0	2	0	2	1	0	0	0	0	0	0	0	1	1	0	0	0	6
7	1	0	0	1	1	0	0	1	0	0	0	0	0	0	1	1	0	0	0	5
7	1	0	0	1	1	1	0	0	0	0	0	0	0	0	1	0	0	0	0	4
7	1	0	0	1	0	0	1	1	0	2	0	0	0	0	0	1	0	0	0	1
7	0	0	1	0	0	0	0	0	1	2	1	0	0	0	0	0	0	0	0	5
7	1	0	1	0	0	0	0	1	0	2	0	0	0	0	0	1	0	0	0	2
7	0	0	1	0	1	2	0	1	0	0	0	0	1	1	0	0	0	0	0	8
7	0	1	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	7
7	0	0	1	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	0	4
8	0	0	0	0	1	1	0	0	1	2	0	1	1	0	0	0	0	0	0	13
8	0	0	0	1	0	1	0	0	0	1	0	1	1	0	0	0	0	1	1	7
8	1	0	1	0	1	1	0	0	0	2	0	0	0	0	0	0	0	0	0	8
8	1	0	1	0	0	1	1	1	0	0	0	0	0	1	1	0	0	1	0	9
9	0	0	0	1	0	1	1	1	0	1	0	0	0	0	0	0	0	0	0	8
8	0	0	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	7
8	0	0	0	1	0	1	1	1	0	1	0	0	1	0	1	1	0	0	0	5
8	0	0	0	1	0	0	0	0	0	0	0	0	2	0	1	1	0	0	1	6
8	0	0	1	0	0	2	0	1	0	1	0	0	0	0	1	1	0	0	0	7
8	1	0	1	0	0	1	0	1	0	0	0	0	0	0	1	1	0	0	0	9
8	0	0	0	2	0	0	0	1	0	1	0	0	0	0	0	0	0	2	0	2
8	0	0	0	4	0	0	0	0	0	0	0	0	1	1	1	1	0	0	1	6
8	0	0	0	1	1	1	0	1	0	0	0	1	1	0	0	1	0	1	0	11
8	0	0	1	2	0	0	0	1	0	0	1	0	0	0	1	1	0	0	0	10
8	0	1	1	1	1	1	0	0	0	0	0	0	0	0	1	1	0	0	0	11
8	1	0	0	1	1	2	0	1	0	0	0	0	0	0	1	0	0	0	0	7
8	0	0	1	0	1	2	0	1	1	1	0	1	0	0	0	0	0	0	0	8
8	0	0	1	0	0	2	1	0	0	0	0	0	1	0	0	0	0	0	0	6
8	0	0	0	1	1	1	0	0	0	0	0	0	0	0	1	1	0	0	2	7
8	0	0	0	1	1	1	0	0	0	1	0	1	0	1	0	1	0	0	0	8
8	0	0	0	1	1	0	0	1	0	2	0	0	0	0	1	1	0	0	0	6
8	1	1	1	0	0	1	1	0	0	0	0	0	0	0	1	1	0	0	0	8
8	0	0	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	9
8	0	0	0	1	1	1	1	0	0	0	2	0	0	0	0	1	0	0	0	10
8	0	0	0	0	0	3	0	1	0	0	1	0	0	0	0	1	0	0	0	9
9	0	0	0	2	1	1	1	1	0	0	1	0	0	0	0	2	0	0	0	13
9	0	0	0	1	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	1
9	0	1	1	0	1	2	0	1	0	1	1	0	0	0	0	1	0	0	0	14
9	0	0	0	1	0	0	0	1	1	2	0	0	0	1	0	1	0	0	0	12
9	1	0	1	1	1	0	0	1	0	0	0	0	1	0	1	2	0	0	0	9
9	0	0	1	0	0	0	0	1	0	0	0	0	1	0	1	0	0	1	1	5
9	0	0	0	2	0	2	0	1	1	2	0	0	0	0	0	1	0	0	0	11
9	1	0	1	0	0	1	0	1	0	2	0	0	0	1	1	0	0	0	0	12
9	0	0	1	0	1	1	0	1	0	0	0	0	0	0	1	0	0	0	0	9
9	0	0	1	0	1	0	1	0	0	2	0	0	0	0	0	0	0	0	0	11
9	1	0	1	0	1	2	0	1	1	0	0	0	0	0	1	1	0	0	0	10
9	0	0	0	1	1	2	0	0	0	0	0	0	0	0	0	0	0	0	0	6
9	0	0	0	1	0	2	0	1	0	1	1	0	0	0	0	1	0	0	0	15
9	0	0	0	0	0	0	0	1	0	0	0	0	1	2	1	1	0	1	0	13
9	1	0	1	1	0	1	0	0	1	0	0	1	0	0	1	0	0	0	0	4
9	0	0	0	0	0	0	0	1	0	0	0	0	0	1	1	0	0	0	2	11
9	0	0	0	2	0	0	1	1	1	1	0	0	0	0	1	1	0	0	0	11
9	0	0	0	1	0	1	0	1	0	0	0	0	0	1	1	0	0	1	0	10
9	0	0	0	1	1	0	1	0	0	0	0	0	1	1	1	0	0	1	1	12
9	1	0	1	0	0	2	0	1	1	0	1	1	0	0	1	0	0	0	0	13
9	1	0	0	1	1	2	0	1	0	0	0	0	0	0	1	0	0	0	0	4
9	0	0	0	1	1	0	0	1	0	0	0	0	0	0	1	1	0	0	0	5
9	0	0	0	1	1	2	0	0	0	0	0	0	1	1	0	1	0	0	0	10
8	0	1	1	0	1	0	1	1	0	0	0	0	0	0	0	0	0	0	0	4
7	0	0	0	0	1	0	0	0	0	2	1	0	1	0	0	0	0	1	0	8
8	0	0	1	0	1	1	0	1	0	1	0	1	0	0	0	0	0	0	0	15
9	1	0	1	0	1	1	0	1	0	1	1	0	0	0	1	0	0	0	0	7
9	1	0	0	1	0	1	0	1	0	0	1	0	0	0	1	1	0	0	0	6
9	1	0	1	0	1	0	1	1	0	2	0	0	0	0	0	1	0	0	0	4
9	0	0	0	1	1	2	0	1	0	0	1	0	0	0	1	1	0	0	0	12
9	0	0	0	1	0	1	1	0	0	0	0	0	0	1	0	0	0	1	1	12
9	0	0	0	1	1	0	1	1	0	0	0	0	1	2	0	0	0	1	0	14
10	0	0	0	1	1	0	1	1	1	2	1	0	0	0	1	1	0	0	0	16
10	1	0	0	1	0	1	1	1	0	0	0	1	0	0	0	1	0	0	0	6
10	0	0	0	1	1	1	0	1	0	0	0	0	1	1	1	1	0	1	0	12
10	0	0	0	1	1	2	1	1	0	0	0	1	0	0	0	0	0	0	0	22
10	0	0	0	1	0	0	0	1	0	1	0	1	1	0	0	0	0	1	2	9
10	1	0	0	2	0	1	0	1	0	0	1	0	0	0	0	1	0	0	1	10
10	1	0	1	0	0	2	0	0	0	0	1	1	1	0	1	1	0	0	0	10
10	0	0	1	1	1	1	1	0	0	0	0	0	0	0	1	0	0	0	0	14
10	0	0	0	1	1	1	0	0	1	2	0	0	0	0	0	2	0	0	0	9
10	1	0	1	0	1	2	0	1	1	2	0	0	0	0	0	0	0	0	0	15
10	1	0	0	1	1	2	1	0	0	1	1	0	0	0	1	0	0	0	0	14
10	0	0	1	0	1	2	0	1	0	1	0	0	0	0	0	1	0	0	0	12
10	1	1	0	1	0	2	1	0	0	0	0	0	0	0	0	2	0	1	0	9
10	0	0	1	0	1	1	1	0	0	1	1	0	1	0	1	0	0	0	1	10
11	0	0	0	1	1	1	1	1	0	0	1	0	1	0	1	1	0	1	0	13
11	1	0	0	1	2	1	1	0	1	0	0	1	1	0	1	1	0	0	0	17
11	0	0	1	0	1	2	0	1	0	2	0	0	0	0	1	1	0	0	0	16
11	0	0	0	1	1	0	1	1	0	1	1	1	1	1	0	1	0	0	0	17
11	0	0	0	0	1	1	0	1	0	0	0	0	1	1	1	1	0	1	1	21
12	0	0	0	1	0	1	1	1	0	1	0	0	1	0	1	0	2	0	0	11
12	0	0	1	1	0	0	0	0	0	0	1	1	2	0	1	2	1	0	0	22
12	0	0	1	0	1	1	1	0	0	0	1	1	1	0	1	1	0	0	0	14
12	0	0	0	0	1	2	0	0	0	0	1	1	1	0	1	1	0	0	0	12
12	0	0	1	0	1	3	0	1	0	1	0	0	0	1	0	1	0	1	0	13
13	0	1	1	1	1	2	0	1	0	0	1	0	0	0	1	1	0	0	0	19
13	1	0	1	0	1	1	0	1	0	0	0	0	2	0	1	0	0	0	0	15
13	1	0	1	0	1	1	0	1	0	0	1	2	1	2	0	0	0	0	0	7
16	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	3 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 time3 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]

3 seconds[/C][/ROW] [ROW]

R Server[/C]

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

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
u[t] = -4.43878 + 0.895405a[t] -1.05312b[t] + 4.39522c[t] + 0.0331238d[t] + 0.193041e[t] + 1.01029f[t] + 1.03118g[t] + 0.595299h[t] + 1.41945i[t] + 1.73936j[t] + 0.435143k[t] + 1.43989l[t] + 0.945066m[t] + 1.73339n[t] + 1.69545o[t] + 0.563802p[t] + 0.65472q[t] + 0.913195r[t] + 0.35854s[t] + 0.718392t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
u[t] =  -4.43878 +  0.895405a[t] -1.05312b[t] +  4.39522c[t] +  0.0331238d[t] +  0.193041e[t] +  1.01029f[t] +  1.03118g[t] +  0.595299h[t] +  1.41945i[t] +  1.73936j[t] +  0.435143k[t] +  1.43989l[t] +  0.945066m[t] +  1.73339n[t] +  1.69545o[t] +  0.563802p[t] +  0.65472q[t] +  0.913195r[t] +  0.35854s[t] +  0.718392t  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]u[t] =  -4.43878 +  0.895405a[t] -1.05312b[t] +  4.39522c[t] +  0.0331238d[t] +  0.193041e[t] +  1.01029f[t] +  1.03118g[t] +  0.595299h[t] +  1.41945i[t] +  1.73936j[t] +  0.435143k[t] +  1.43989l[t] +  0.945066m[t] +  1.73339n[t] +  1.69545o[t] +  0.563802p[t] +  0.65472q[t] +  0.913195r[t] +  0.35854s[t] +  0.718392t  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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
u[t] = -4.43878 + 0.895405a[t] -1.05312b[t] + 4.39522c[t] + 0.0331238d[t] + 0.193041e[t] + 1.01029f[t] + 1.03118g[t] + 0.595299h[t] + 1.41945i[t] + 1.73936j[t] + 0.435143k[t] + 1.43989l[t] + 0.945066m[t] + 1.73339n[t] + 1.69545o[t] + 0.563802p[t] + 0.65472q[t] + 0.913195r[t] + 0.35854s[t] + 0.718392t + 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)	-4.439	0.3933	-1.1280e+01	1.201e-24	6.004e-25
a	+0.8954	0.13	+6.8880e+00	3.64e-11	1.82e-11
b	-1.053	0.4541	-2.3190e+00	0.02109	0.01054
c	+4.395	0.9015	+4.8750e+00	1.811e-06	9.055e-07
d	+0.03312	0.4066	+8.1460e-02	0.9351	0.4676
e	+0.193	0.3228	+5.9800e-01	0.5503	0.2752
f	+1.01	0.3319	+3.0440e+00	0.002556	0.001278
g	+1.031	0.2644	+3.9000e+00	0.0001204	6.018e-05
h	+0.5953	0.3734	+1.5940e+00	0.112	0.05599
i	+1.419	0.3524	+4.0280e+00	7.24e-05	3.62e-05
j	+1.739	0.6532	+2.6630e+00	0.008194	0.004097
k	+0.4351	0.289	+1.5050e+00	0.1333	0.06666
l	+1.44	0.467	+3.0830e+00	0.002249	0.001125
m	+0.9451	0.4704	+2.0090e+00	0.04549	0.02275
n	+1.733	0.4056	+4.2740e+00	2.622e-05	1.311e-05
o	+1.695	0.3854	+4.3990e+00	1.538e-05	7.689e-06
p	+0.5638	0.353	+1.5970e+00	0.1114	0.05569
q	+0.6547	0.3267	+2.0040e+00	0.04604	0.02302
r	+0.9132	0.8035	+1.1360e+00	0.2567	0.1284
s	+0.3585	0.4533	+7.9090e-01	0.4297	0.2148
t	+0.7184	0.4401	+1.6320e+00	0.1037	0.05187

\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) & -4.439 &  0.3933 & -1.1280e+01 &  1.201e-24 &  6.004e-25 \tabularnewline
a & +0.8954 &  0.13 & +6.8880e+00 &  3.64e-11 &  1.82e-11 \tabularnewline
b & -1.053 &  0.4541 & -2.3190e+00 &  0.02109 &  0.01054 \tabularnewline
c & +4.395 &  0.9015 & +4.8750e+00 &  1.811e-06 &  9.055e-07 \tabularnewline
d & +0.03312 &  0.4066 & +8.1460e-02 &  0.9351 &  0.4676 \tabularnewline
e & +0.193 &  0.3228 & +5.9800e-01 &  0.5503 &  0.2752 \tabularnewline
f & +1.01 &  0.3319 & +3.0440e+00 &  0.002556 &  0.001278 \tabularnewline
g & +1.031 &  0.2644 & +3.9000e+00 &  0.0001204 &  6.018e-05 \tabularnewline
h & +0.5953 &  0.3734 & +1.5940e+00 &  0.112 &  0.05599 \tabularnewline
i & +1.419 &  0.3524 & +4.0280e+00 &  7.24e-05 &  3.62e-05 \tabularnewline
j & +1.739 &  0.6532 & +2.6630e+00 &  0.008194 &  0.004097 \tabularnewline
k & +0.4351 &  0.289 & +1.5050e+00 &  0.1333 &  0.06666 \tabularnewline
l & +1.44 &  0.467 & +3.0830e+00 &  0.002249 &  0.001125 \tabularnewline
m & +0.9451 &  0.4704 & +2.0090e+00 &  0.04549 &  0.02275 \tabularnewline
n & +1.733 &  0.4056 & +4.2740e+00 &  2.622e-05 &  1.311e-05 \tabularnewline
o & +1.695 &  0.3854 & +4.3990e+00 &  1.538e-05 &  7.689e-06 \tabularnewline
p & +0.5638 &  0.353 & +1.5970e+00 &  0.1114 &  0.05569 \tabularnewline
q & +0.6547 &  0.3267 & +2.0040e+00 &  0.04604 &  0.02302 \tabularnewline
r & +0.9132 &  0.8035 & +1.1360e+00 &  0.2567 &  0.1284 \tabularnewline
s & +0.3585 &  0.4533 & +7.9090e-01 &  0.4297 &  0.2148 \tabularnewline
t & +0.7184 &  0.4401 & +1.6320e+00 &  0.1037 &  0.05187 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]-4.439[/C][C] 0.3933[/C][C]-1.1280e+01[/C][C] 1.201e-24[/C][C] 6.004e-25[/C][/ROW]
[ROW][C]a[/C][C]+0.8954[/C][C] 0.13[/C][C]+6.8880e+00[/C][C] 3.64e-11[/C][C] 1.82e-11[/C][/ROW]
[ROW][C]b[/C][C]-1.053[/C][C] 0.4541[/C][C]-2.3190e+00[/C][C] 0.02109[/C][C] 0.01054[/C][/ROW]
[ROW][C]c[/C][C]+4.395[/C][C] 0.9015[/C][C]+4.8750e+00[/C][C] 1.811e-06[/C][C] 9.055e-07[/C][/ROW]
[ROW][C]d[/C][C]+0.03312[/C][C] 0.4066[/C][C]+8.1460e-02[/C][C] 0.9351[/C][C] 0.4676[/C][/ROW]
[ROW][C]e[/C][C]+0.193[/C][C] 0.3228[/C][C]+5.9800e-01[/C][C] 0.5503[/C][C] 0.2752[/C][/ROW]
[ROW][C]f[/C][C]+1.01[/C][C] 0.3319[/C][C]+3.0440e+00[/C][C] 0.002556[/C][C] 0.001278[/C][/ROW]
[ROW][C]g[/C][C]+1.031[/C][C] 0.2644[/C][C]+3.9000e+00[/C][C] 0.0001204[/C][C] 6.018e-05[/C][/ROW]
[ROW][C]h[/C][C]+0.5953[/C][C] 0.3734[/C][C]+1.5940e+00[/C][C] 0.112[/C][C] 0.05599[/C][/ROW]
[ROW][C]i[/C][C]+1.419[/C][C] 0.3524[/C][C]+4.0280e+00[/C][C] 7.24e-05[/C][C] 3.62e-05[/C][/ROW]
[ROW][C]j[/C][C]+1.739[/C][C] 0.6532[/C][C]+2.6630e+00[/C][C] 0.008194[/C][C] 0.004097[/C][/ROW]
[ROW][C]k[/C][C]+0.4351[/C][C] 0.289[/C][C]+1.5050e+00[/C][C] 0.1333[/C][C] 0.06666[/C][/ROW]
[ROW][C]l[/C][C]+1.44[/C][C] 0.467[/C][C]+3.0830e+00[/C][C] 0.002249[/C][C] 0.001125[/C][/ROW]
[ROW][C]m[/C][C]+0.9451[/C][C] 0.4704[/C][C]+2.0090e+00[/C][C] 0.04549[/C][C] 0.02275[/C][/ROW]
[ROW][C]n[/C][C]+1.733[/C][C] 0.4056[/C][C]+4.2740e+00[/C][C] 2.622e-05[/C][C] 1.311e-05[/C][/ROW]
[ROW][C]o[/C][C]+1.695[/C][C] 0.3854[/C][C]+4.3990e+00[/C][C] 1.538e-05[/C][C] 7.689e-06[/C][/ROW]
[ROW][C]p[/C][C]+0.5638[/C][C] 0.353[/C][C]+1.5970e+00[/C][C] 0.1114[/C][C] 0.05569[/C][/ROW]
[ROW][C]q[/C][C]+0.6547[/C][C] 0.3267[/C][C]+2.0040e+00[/C][C] 0.04604[/C][C] 0.02302[/C][/ROW]
[ROW][C]r[/C][C]+0.9132[/C][C] 0.8035[/C][C]+1.1360e+00[/C][C] 0.2567[/C][C] 0.1284[/C][/ROW]
[ROW][C]s[/C][C]+0.3585[/C][C] 0.4533[/C][C]+7.9090e-01[/C][C] 0.4297[/C][C] 0.2148[/C][/ROW]
[ROW][C]t[/C][C]+0.7184[/C][C] 0.4401[/C][C]+1.6320e+00[/C][C] 0.1037[/C][C] 0.05187[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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)	-4.439	0.3933	-1.1280e+01	1.201e-24	6.004e-25
a	+0.8954	0.13	+6.8880e+00	3.64e-11	1.82e-11
b	-1.053	0.4541	-2.3190e+00	0.02109	0.01054
c	+4.395	0.9015	+4.8750e+00	1.811e-06	9.055e-07
d	+0.03312	0.4066	+8.1460e-02	0.9351	0.4676
e	+0.193	0.3228	+5.9800e-01	0.5503	0.2752
f	+1.01	0.3319	+3.0440e+00	0.002556	0.001278
g	+1.031	0.2644	+3.9000e+00	0.0001204	6.018e-05
h	+0.5953	0.3734	+1.5940e+00	0.112	0.05599
i	+1.419	0.3524	+4.0280e+00	7.24e-05	3.62e-05
j	+1.739	0.6532	+2.6630e+00	0.008194	0.004097
k	+0.4351	0.289	+1.5050e+00	0.1333	0.06666
l	+1.44	0.467	+3.0830e+00	0.002249	0.001125
m	+0.9451	0.4704	+2.0090e+00	0.04549	0.02275
n	+1.733	0.4056	+4.2740e+00	2.622e-05	1.311e-05
o	+1.695	0.3854	+4.3990e+00	1.538e-05	7.689e-06
p	+0.5638	0.353	+1.5970e+00	0.1114	0.05569
q	+0.6547	0.3267	+2.0040e+00	0.04604	0.02302
r	+0.9132	0.8035	+1.1360e+00	0.2567	0.1284
s	+0.3585	0.4533	+7.9090e-01	0.4297	0.2148
t	+0.7184	0.4401	+1.6320e+00	0.1037	0.05187

Multiple Linear Regression - Regression Statistics
Multiple R	0.8737
R-squared	0.7634
Adjusted R-squared	0.7467
F-TEST (value)	45.81
F-TEST (DF numerator)	20
F-TEST (DF denominator)	284
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.616
Sum Squared Residuals	1943

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.8737 \tabularnewline
R-squared &  0.7634 \tabularnewline
Adjusted R-squared &  0.7467 \tabularnewline
F-TEST (value) &  45.81 \tabularnewline
F-TEST (DF numerator) & 20 \tabularnewline
F-TEST (DF denominator) & 284 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.616 \tabularnewline
Sum Squared Residuals &  1943 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.8737[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.7634[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.7467[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 45.81[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]20[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]284[/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] 2.616[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1943[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	0.8737
R-squared	0.7634
Adjusted R-squared	0.7467
F-TEST (value)	45.81
F-TEST (DF numerator)	20
F-TEST (DF denominator)	284
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.616
Sum Squared Residuals	1943

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=&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=&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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 = 33.675, df1 = 2, df2 = 282, p-value = 7.677e-14
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 2.2217, df1 = 40, df2 = 244, p-value = 0.0001184
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 25.66, df1 = 2, df2 = 282, p-value = 5.777e-11

\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 = 33.675, df1 = 2, df2 = 282, p-value = 7.677e-14
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 2.2217, df1 = 40, df2 = 244, p-value = 0.0001184
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 25.66, df1 = 2, df2 = 282, p-value = 5.777e-11
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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 = 33.675, df1 = 2, df2 = 282, p-value = 7.677e-14
[/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 = 2.2217, df1 = 40, df2 = 244, p-value = 0.0001184
[/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 = 25.66, df1 = 2, df2 = 282, p-value = 5.777e-11
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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 = 33.675, df1 = 2, df2 = 282, p-value = 7.677e-14
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 2.2217, df1 = 40, df2 = 244, p-value = 0.0001184
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 25.66, df1 = 2, df2 = 282, p-value = 5.777e-11

Variance Inflation Factors (Multicollinearity)

> vif
       a        b        c        d        e        f        g        h 
5.369180 1.177037 1.148947 1.542788 1.607982 1.352881 1.766587 1.193242 
       i        j        k        l        m        n        o        p 
1.395528 1.326248 1.683763 1.211688 1.347241 1.579727 1.283522 1.337040 
       q        r        s        t 
1.464835 1.112602 1.314406 1.295375

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
       a        b        c        d        e        f        g        h 
5.369180 1.177037 1.148947 1.542788 1.607982 1.352881 1.766587 1.193242 
       i        j        k        l        m        n        o        p 
1.395528 1.326248 1.683763 1.211688 1.347241 1.579727 1.283522 1.337040 
       q        r        s        t 
1.464835 1.112602 1.314406 1.295375 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=6

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
       a        b        c        d        e        f        g        h 
5.369180 1.177037 1.148947 1.542788 1.607982 1.352881 1.766587 1.193242 
       i        j        k        l        m        n        o        p 
1.395528 1.326248 1.683763 1.211688 1.347241 1.579727 1.283522 1.337040 
       q        r        s        t 
1.464835 1.112602 1.314406 1.295375 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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 
5.369180 1.177037 1.148947 1.542788 1.607982 1.352881 1.766587 1.193242 
       i        j        k        l        m        n        o        p 
1.395528 1.326248 1.683763 1.211688 1.347241 1.579727 1.283522 1.337040 
       q        r        s        t 
1.464835 1.112602 1.314406 1.295375

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 = 21 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par6 = 12 ;

Parameters (R input):

par1 = 21 ; 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