Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sat, 16 Dec 2017 18:43:08 +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/2017/Dec/16/t15134462724xhrpcpyalvpdri.htm/, Retrieved Thu, 16 May 2024 02:38:36 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=309930, Retrieved Thu, 16 May 2024 02:38:36 +0000

QR Codes:

Paste this QR Code to cite your computation.

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] [Datareeks 1.1 - M...] [2017-12-16 17:43:08] [228f385b091a4ec8014a0b8722ae7714] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

34	0	0	5	20	2	3
37	0	1	8	26	4	5
38	0	1	8	27	4	4
38	0	1	10	22	4	4
32	0	1	8	23	4	5
40	1	1	8	26	4	5
39	0	1	8	25	5	5
32	0	0	8	25	4	5
35	0	0	6	21	3	5
36	0	1	5	23	5	5
35	1	1	7	21	4	4
34	1	0	8	25	4	3
33	1	1	7	25	4	4
36	0	0	8	26	4	5
32	0	0	6	21	4	4
42	1	1	7	26	3	3
35	0	0	8	22	4	5
42	1	1	8	23	4	5
42	1	1	10	28	5	5
34	0	0	4	23	4	3
38	0	1	6	24	4	5
23	0	0	6	19	3	4
38	0	0	7	25	4	5
32	0	1	8	23	2	3
33	1	0	7	20	4	4
32	1	1	4	19	3	4
39	0	1	7	21	5	4
36	0	0	10	26	5	5
34	0	0	7	24	3	5
33	0	0	6	24	3	3
36	1	1	8	27	4	1
30	0	0	6	21	4	5
26	0	1	2	11	4	5
41	0	1	8	27	5	3
33	0	1	4	27	4	5
33	1	1	6	21	4	4
24	1	0	8	20	3	4
33	1	1	4	22	3	4
28	1	0	6	19	3	4
33	1	1	6	19	3	4
40	1	0	8	20	3	5
37	0	0	7	26	4	4
29	1	1	4	19	3	3
36	0	0	8	26	4	5
35	0	0	6	23	5	4
25	0	1	8	27	5	4
40	0	1	7	28	4	5
30	0	1	8	23	3	5
34	0	1	4	25	4	5
36	0	1	8	21	4	3
41	0	1	10	29	5	4
23	1	0	6	22	4	4
36	0	0	6	20	4	4
33	1	0	8	21	4	5
35	1	1	7	23	4	2
35	1	1	8	25	4	4
37	0	0	8	21	4	3
37	1	1	8	27	3	5
24	0	0	5	16	4	5
40	0	0	8	26	4	5
30	0	1	6	22	4	5
31	0	1	6	24	4	4
32	0	1	8	24	4	4
44	0	1	6	26	4	5
28	1	0	6	21	3	4
38	0	1	6	25	4	4
35	0	1	7	25	3	4
39	0	1	8	24	4	4
40	0	1	8	30	5	4
26	0	1	7	19	5	4
21	1	1	4	23	2	4
36	0	0	6	19	3	3
42	1	1	8	28	5	4
41	0	1	6	28	4	4
33	0	0	7	24	4	3
27	0	1	5	21	4	5
31	0	0	5	21	3	4
35	0	1	8	23	4	5
34	0	1	5	16	4	3
36	0	0	8	24	5	5
31	0	0	5	17	2	5
32	0	1	6	22	4	4
33	0	0	6	25	4	3
43	1	0	6	29	5	5
43	0	1	8	25	4	5
33	1	1	4	25	4	5
36	0	0	7	23	3	4
33	0	0	6	24	4	3
28	0	0	5	18	2	4
38	0	1	8	24	4	3
38	0	1	6	25	4	4
37	0	1	7	25	5	5
36	0	1	6	26	4	4
30	1	0	10	24	4	4
30	0	0	4	20	2	4
28	0	0	8	25	5	5
44	1	0	4	26	4	5
26	1	0	4	26	3	4
33	0	0	7	24	3	4
38	0	1	4	24	4	4
31	0	1	4	22	4	5
33	0	0	5	20	3	4
32	0	0	8	24	3	4
33	0	0	8	21	4	4
38	0	1	8	26	4	5
39	0	1	8	25	4	5
34	0	0	6	20	4	4
33	0	1	4	24	4	5
28	0	1	6	18	3	3
35	0	0	7	22	4	4
17	1	1	6	17	3	3
26	1	1	5	23	4	5
34	1	1	4	20	3	4
34	1	1	4	20	3	4
38	0	1	6	22	3	5
40	1	1	7	21	4	5
45	0	1	10	28	5	5
24	0	0	6	16	2	3
21	0	1	8	22	4	5
31	0	1	6	23	4	3
35	0	1	4	21	4	4
35	0	1	8	21	4	4
19	1	0	4	16	2	4
22	1	0	5	17	3	4
22	1	0	5	21	3	4
37	0	0	6	24	4	4
37	1	1	6	23	3	5
40	1	1	6	23	4	5
25	0	0	4	19	3	5
28	0	1	6	20	4	3
22	1	0	6	16	4	4
28	1	1	4	23	4	4
38	1	1	6	24	4	4
22	1	1	7	19	3	4
34	1	0	5	22	4	4
37	1	1	8	22	5	4
35	1	1	8	25	4	5
31	1	1	6	23	4	4
23	1	1	3	16	4	5
34	1	0	6	23	4	5
15	1	1	6	17	4	4
39	1	1	8	23	3	4
36	1	1	6	24	4	3
27	1	1	2	21	4	4
28	0	0	6	19	3	4
20	0	1	6	17	2	3
21	0	1	4	19	4	3
40	0	1	8	27	4	3
35	1	1	8	24	4	4
36	0	0	8	20	4	4
32	1	0	8	20	2	4
34	0	0	8	22	4	4
25	1	0	6	20	4	4
34	1	1	4	25	3	5
37	0	0	7	23	4	5
42	1	1	8	28	5	5
33	1	0	6	25	3	5
35	1	1	7	23	5	5
38	1	1	10	27	4	5
27	0	0	4	21	3	5
32	0	1	5	22	2	3
32	0	0	6	24	5	5
22	1	0	2	18	2	4
20	1	1	6	15	3	4
26	0	0	5	22	2	5
30	0	1	5	22	5	3
35	1	1	8	24	4	4
38	0	0	8	26	4	4
26	1	1	2	18	4	4
35	1	0	6	24	3	4
35	0	0	6	22	4	4
42	0	1	9	26	5	5
35	0	0	8	23	4	4
38	1	0	8	24	5	4
35	0	1	8	22	3	5
31	1	1	4	22	3	4
37	1	1	6	23	4	5
33	1	1	8	25	4	5
36	1	0	8	24	4	4
27	1	1	8	23	3	4
36	1	0	6	22	4	4
23	0	0	6	18	3	4
17	0	1	2	18	3	3
37	0	1	8	24	4	5
32	1	0	5	22	4	5
33	1	0	7	24	4	4
38	0	0	7	27	4	5
42	0	0	8	24	4	4
30	1	1	6	23	3	4
38	1	0	6	25	4	5
32	1	1	6	19	4	3
35	0	1	9	22	4	5
33	1	0	7	24	3	5
24	1	1	6	20	3	3
22	0	1	4	20	2	3
28	0	1	4	23	3	3
32	1	1	6	20	4	3
33	0	1	8	22	3	3
38	1	1	8	24	5	4
34	1	1	8	24	4	3
35	1	1	8	24	4	4
27	0	1	6	17	3	4
21	1	0	5	21	2	5
36	1	1	8	22	5	5
28	1	1	4	22	4	4
36	1	0	8	24	4	5
27	1	0	7	23	3	5
37	1	1	8	24	3	5
31	1	0	10	27	4	5
34	1	0	7	22	4	4
34	1	0	7	22	4	4
22	1	1	2	17	3	3
37	1	1	6	24	4	5
29	1	1	3	21	4	4
31	1	1	10	25	5	5
40	1	0	8	23	4	5
36	1	1	6	26	3	4
37	1	1	4	23	3	5
22	0	0	7	19	4	5
35	1	1	8	26	4	4
31	1	0	6	25	4	4
17	1	1	4	17	3	4
32	1	0	8	23	4	4
32	1	1	6	22	3	4
32	1	1	8	23	4	5
24	1	0	5	21	4	5
34	1	1	10	24	3	5
36	0	0	8	23	4	5
27	1	0	6	20	4	4
43	1	1	10	28	5	5
30	1	1	6	17	3	4
25	1	0	6	21	3	4
36	1	0	6	23	4	4
33	1	0	8	23	4	5
35	1	0	8	24	4	4
35	1	0	8	24	4	4
35	1	0	8	24	4	4
35	1	0	8	25	4	5
28	1	1	6	22	3	5
38	1	1	5	25	4	5
29	1	1	4	18	4	4
25	0	1	4	15	3	4
39	1	0	5	23	5	4
18	1	1	3	12	2	2
38	0	0	9	27	4	4
22	0	1	2	12	1	3
19	0	0	2	15	3	2
19	0	0	3	14	2	2
35	1	0	8	26	4	5
31	1	1	6	24	4	5
27	1	1	6	18	3	3
35	1	1	9	25	4	3
42	0	1	9	23	5	4
14	1	0	2	10	2	3
26	1	1	8	20	3	3
36	1	0	7	26	4	4
27	1	1	6	18	3	3
26	1	1	4	18	3	4
18	0	0	4	16	2	2
27	1	1	6	17	4	4
19	1	1	4	19	3	3
27	1	1	6	18	3	3
36	0	1	8	24	4	4
39	1	1	9	23	5	3
39	1	1	7	25	4	5
35	0	1	6	26	4	4
41	1	1	8	24	4	5
28	1	0	6	26	4	4
32	1	1	4	26	5	5
34	1	1	8	22	4	4
36	1	1	8	25	4	5
20	1	0	6	24	4	4
42	1	1	8	27	5	5
38	1	1	4	28	5	5
40	1	1	6	25	4	4
20	1	0	6	16	3	3
35	1	0	10	24	4	4
35	1	0	8	25	5	4
28	1	0	6	25	4	4
33	1	1	8	23	4	4
22	1	0	6	20	4	5
32	1	1	8	22	4	5
34	1	1	9	25	5	4
45	1	1	6	28	5	5
26	1	1	6	22	4	4
32	1	0	8	23	4	3
31	1	0	8	19	4	3
37	1	1	8	26	3	3
31	1	0	5	27	5	5
31	1	1	7	21	4	3
34	1	1	8	23	4	4
44	1	1	8	28	5	4
34	1	0	5	25	3	4
35	1	0	6	23	3	5
31	1	1	7	20	3	3
32	1	1	8	19	4	5
33	1	0	5	18	4	3
30	0	1	10	25	4	4
41	1	1	5	25	4	5
35	1	0	6	24	4	3
32	1	0	6	22	3	3
35	1	0	6	20	4	4
33	1	0	8	19	4	5
29	1	0	6	18	3	3
32	1	1	6	25	4	4
33	1	1	8	25	5	4
40	1	1	10	26	5	5
36	1	1	6	24	4	3
36	0	0	6	24	4	4
32	1	0	4	20	4	3
23	1	0	8	24	4	3
28	1	1	8	19	4	4
33	1	1	8	25	4	5
31	1	1	6	20	4	3
29	1	1	7	18	4	3
27	1	1	8	21	4	3
34	1	1	7	23	4	4
25	1	0	6	17	4	3
34	1	0	9	26	5	5
41	1	0	10	29	4	5
32	1	0	8	21	3	4
32	1	0	6	23	4	4
30	1	0	8	21	4	4
35	1	1	6	22	4	4
24	0	0	6	19	2	3
32	1	0	9	19	4	3
35	1	1	6	26	4	4
36	0	1	8	26	4	3
38	0	0	6	19	3	4
27	1	0	4	19	4	4
31	1	0	4	20	4	3
32	1	0	4	23	4	3
34	1	0	6	24	5	3
33	1	0	6	19	5	3
30	1	0	6	22	4	4
30	1	1	5	26	5	5
34	1	1	6	23	4	4
40	1	1	10	29	5	5
31	0	0	6	21	4	4
17	0	1	2	12	4	4
19	0	1	2	13	5	4
24	0	1	5	15	1	4
31	0	1	2	21	2	3
20	0	0	6	13	4	3
23	0	0	4	21	4	3
35	1	0	8	23	4	3
34	1	0	8	24	4	4
29	0	0	2	23	5	4
35	1	1	8	24	5	5
45	1	1	6	27	5	5
45	1	1	6	26	5	3
37	1	1	8	25	4	4
32	1	1	8	21	3	4
34	1	0	6	17	3	3
31	1	1	8	21	5	5
43	1	1	8	25	5	5
33	1	0	4	26	4	5
31	1	1	8	25	4	4
21	1	1	5	20	3	3
41	1	1	8	22	4	4
31	1	0	8	19	4	4
27	1	1	7	21	3	3
31	1	0	8	23	4	3
22	1	1	8	22	4	4
31	1	0	9	22	4	3
25	1	0	4	21	5	3
27	1	1	7	23	3	4
31	1	0	10	24	5	3
31	1	1	7	25	5	5
43	1	1	10	27	5	5
33	1	0	8	24	4	3
30	0	0	8	19	4	4
40	1	1	10	26	3	5
28	1	0	9	25	4	4
36	0	1	8	23	4	5
37	0	1	9	23	5	4
32	1	0	7	22	3	3
33	1	1	10	28	3	4
35	1	0	6	19	4	4
35	1	1	8	27	4	4
28	1	0	4	18	3	3
28	1	0	7	17	3	3
35	1	1	6	25	5	5
42	1	1	8	25	5	4
34	1	1	10	26	4	5
32	0	0	4	21	2	3
30	1	0	7	21	3	3
34	1	0	5	22	4	4
29	1	0	6	22	4	4
27	1	0	6	19	3	3
36	1	1	8	24	3	4
30	1	0	10	26	4	3
31	0	1	8	21	5	3
44	1	0	10	28	5	5
40	0	1	8	24	5	3
30	1	0	7	18	4	2
33	0	1	6	22	2	3
29	1	0	6	24	3	4
24	1	1	6	19	5	3
23	0	0	6	18	3	3
32	1	0	7	23	3	3
27	0	0	4	21	4	3
30	0	0	9	18	5	3
33	0	1	8	25	5	5
21	0	1	4	18	3	4
35	1	0	8	26	4	4
20	0	0	5	20	4	3
36	0	1	8	23	4	5
32	0	1	7	18	3	4
35	0	0	7	26	5	5
28	0	0	4	14	3	4
24	0	0	4	18	3	4
28	1	0	7	19	2	4
28	0	1	3	24	3	5
34	0	1	8	25	5	3
32	0	0	8	23	5	4
32	0	1	9	19	4	5
30	1	0	6	21	4	4
25	0	1	3	18	3	4
34	0	0	7	24	4	4
11	0	1	2	16	3	5
32	1	1	9	24	4	4
35	1	1	6	25	4	3
28	1	0	8	18	4	4
24	0	1	4	17	5	4
33	0	1	8	20	4	3
36	1	1	7	21	3	4
27	1	0	8	20	3	3
33	0	1	8	20	4	3
44	1	0	8	26	4	4
26	0	1	8	24	5	5
21	0	0	6	23	4	5
29	1	1	8	19	3	4
39	0	0	6	26	5	4
44	1	1	10	28	5	4
27	1	1	8	21	5	5
28	0	1	6	18	4	4
35	0	1	9	24	4	4
34	1	1	6	24	4	4
32	1	1	10	24	5	5
31	1	0	4	23	3	4
25	0	0	10	21	5	4
31	1	0	6	23	4	4
36	1	1	8	26	5	5
36	0	0	8	24	4	4
36	0	0	6	22	4	4

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	10 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 time10 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309930&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]

10 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=309930&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309930&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	10 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
Productiviteit[t] = + 2.03606 -0.602392Groep[t] + 0.617175Geslacht[t] + 0.553453Design[t] + 1.02083Workflow[t] + 0.677184Website_Functions[t] + 0.28695Computations_Reproducability[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Productiviteit[t] =  +  2.03606 -0.602392Groep[t] +  0.617175Geslacht[t] +  0.553453Design[t] +  1.02083Workflow[t] +  0.677184Website_Functions[t] +  0.28695Computations_Reproducability[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309930&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Productiviteit[t] =  +  2.03606 -0.602392Groep[t] +  0.617175Geslacht[t] +  0.553453Design[t] +  1.02083Workflow[t] +  0.677184Website_Functions[t] +  0.28695Computations_Reproducability[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309930&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309930&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
Productiviteit[t] = + 2.03606 -0.602392Groep[t] + 0.617175Geslacht[t] + 0.553453Design[t] + 1.02083Workflow[t] + 0.677184Website_Functions[t] + 0.28695Computations_Reproducability[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)	+2.036	1.488	+1.3680e+00	0.1719	0.08593
Groep	-0.6024	0.4048	-1.4880e+00	0.1375	0.06873
Geslacht	+0.6172	0.4002	+1.5420e+00	0.1237	0.06187
Design	+0.5534	0.1266	+4.3710e+00	1.546e-05	7.732e-06
Workflow	+1.021	0.07594	+1.3440e+01	9.682e-35	4.841e-35
Website_Functions	+0.6772	0.2893	+2.3410e+00	0.0197	0.009852
Computations_Reproducability	+0.2869	0.2723	+1.0540e+00	0.2926	0.1463

\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) & +2.036 &  1.488 & +1.3680e+00 &  0.1719 &  0.08593 \tabularnewline
Groep & -0.6024 &  0.4048 & -1.4880e+00 &  0.1375 &  0.06873 \tabularnewline
Geslacht & +0.6172 &  0.4002 & +1.5420e+00 &  0.1237 &  0.06187 \tabularnewline
Design & +0.5534 &  0.1266 & +4.3710e+00 &  1.546e-05 &  7.732e-06 \tabularnewline
Workflow & +1.021 &  0.07594 & +1.3440e+01 &  9.682e-35 &  4.841e-35 \tabularnewline
Website_Functions & +0.6772 &  0.2893 & +2.3410e+00 &  0.0197 &  0.009852 \tabularnewline
Computations_Reproducability & +0.2869 &  0.2723 & +1.0540e+00 &  0.2926 &  0.1463 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309930&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]+2.036[/C][C] 1.488[/C][C]+1.3680e+00[/C][C] 0.1719[/C][C] 0.08593[/C][/ROW]
[ROW][C]Groep[/C][C]-0.6024[/C][C] 0.4048[/C][C]-1.4880e+00[/C][C] 0.1375[/C][C] 0.06873[/C][/ROW]
[ROW][C]Geslacht[/C][C]+0.6172[/C][C] 0.4002[/C][C]+1.5420e+00[/C][C] 0.1237[/C][C] 0.06187[/C][/ROW]
[ROW][C]Design[/C][C]+0.5534[/C][C] 0.1266[/C][C]+4.3710e+00[/C][C] 1.546e-05[/C][C] 7.732e-06[/C][/ROW]
[ROW][C]Workflow[/C][C]+1.021[/C][C] 0.07594[/C][C]+1.3440e+01[/C][C] 9.682e-35[/C][C] 4.841e-35[/C][/ROW]
[ROW][C]Website_Functions[/C][C]+0.6772[/C][C] 0.2893[/C][C]+2.3410e+00[/C][C] 0.0197[/C][C] 0.009852[/C][/ROW]
[ROW][C]Computations_Reproducability[/C][C]+0.2869[/C][C] 0.2723[/C][C]+1.0540e+00[/C][C] 0.2926[/C][C] 0.1463[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309930&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309930&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)	+2.036	1.488	+1.3680e+00	0.1719	0.08593
Groep	-0.6024	0.4048	-1.4880e+00	0.1375	0.06873
Geslacht	+0.6172	0.4002	+1.5420e+00	0.1237	0.06187
Design	+0.5534	0.1266	+4.3710e+00	1.546e-05	7.732e-06
Workflow	+1.021	0.07594	+1.3440e+01	9.682e-35	4.841e-35
Website_Functions	+0.6772	0.2893	+2.3410e+00	0.0197	0.009852
Computations_Reproducability	+0.2869	0.2723	+1.0540e+00	0.2926	0.1463

Multiple Linear Regression - Regression Statistics
Multiple R	0.7319
R-squared	0.5357
Adjusted R-squared	0.5294
F-TEST (value)	84.42
F-TEST (DF numerator)	6
F-TEST (DF denominator)	439
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	4.156
Sum Squared Residuals	7581

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.7319 \tabularnewline
R-squared &  0.5357 \tabularnewline
Adjusted R-squared &  0.5294 \tabularnewline
F-TEST (value) &  84.42 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 439 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  4.156 \tabularnewline
Sum Squared Residuals &  7581 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309930&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.7319[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.5357[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.5294[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 84.42[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]439[/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] 4.156[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 7581[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309930&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309930&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	0.7319
R-squared	0.5357
Adjusted R-squared	0.5294
F-TEST (value)	84.42
F-TEST (DF numerator)	6
F-TEST (DF denominator)	439
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	4.156
Sum Squared Residuals	7581

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=309930&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=309930&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309930&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 = 0.23454, df1 = 2, df2 = 437, p-value = 0.791
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.0429, df1 = 12, df2 = 427, p-value = 0.4081
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.32482, df1 = 2, df2 = 437, p-value = 0.7228

\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 = 0.23454, df1 = 2, df2 = 437, p-value = 0.791
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.0429, df1 = 12, df2 = 427, p-value = 0.4081
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.32482, df1 = 2, df2 = 437, p-value = 0.7228
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309930&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 = 0.23454, df1 = 2, df2 = 437, p-value = 0.791
[/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 = 1.0429, df1 = 12, df2 = 427, p-value = 0.4081
[/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 = 0.32482, df1 = 2, df2 = 437, p-value = 0.7228
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309930&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309930&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 = 0.23454, df1 = 2, df2 = 437, p-value = 0.791
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.0429, df1 = 12, df2 = 427, p-value = 0.4081
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.32482, df1 = 2, df2 = 437, p-value = 0.7228

Variance Inflation Factors (Multicollinearity)
> vif Groep Geslacht 1.013015 1.021959 Design Workflow 1.396227 1.663558 Website_Functions Computations_Reproducability 1.364783 1.172878

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
                       Groep                     Geslacht 
                    1.013015                     1.021959 
                      Design                     Workflow 
                    1.396227                     1.663558 
           Website_Functions Computations_Reproducability 
                    1.364783                     1.172878 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309930&T=6

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
                       Groep                     Geslacht 
                    1.013015                     1.021959 
                      Design                     Workflow 
                    1.396227                     1.663558 
           Website_Functions Computations_Reproducability 
                    1.364783                     1.172878 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309930&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309930&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 Groep Geslacht 1.013015 1.021959 Design Workflow 1.396227 1.663558 Website_Functions Computations_Reproducability 1.364783 1.172878

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

Postscript link

PDF link

Figure 6

PNG link

Postscript link

PDF link

Figure 7

PNG link

Postscript link

PDF link

Figure 8

PNG link

Postscript link

PDF link

Figure 9

PNG link

Postscript link

PDF link

Figure 10

PNG link

Postscript link

PDF link

Parameters (Session):

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 12 ;

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

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