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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

Mon, 15 Dec 2014 16:39:41 +0000

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/2014/Dec/15/t1418661589kna0u7o9qc4xzjd.htm/, Retrieved Thu, 16 May 2024 21:49:55 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=268737, Retrieved Thu, 16 May 2024 21:49:55 +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)

-     [Stem-and-leaf Plot] [] [2013-10-01 09:27:37] [0307e7a6407eb638caabc417e3a6b260]
- RMPD    [Multiple Regression] [] [2014-12-15 16:39:41] [6fc1b517ba5ef695988bbc0a377c4b82] [Current]

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9	8	5	5	4	5	2	1	0	1	2	1
9	8	6	5	6	5	2	0	1	0	0	2
8	8	4	7	6	4	2	0	0	2	0	2
8	7	4	3	5	0	2	0	1	1	0	0
8	9	4	4	3	3	2	2	0	2	1	2
8	8	5	7	4	5	1	0	0	1	0	0
8	7	3	6	1	2	2	2	0	1	0	1
6	9	7	6	4	3	1	3	1	0	2	2
7	7	2	2	4	4	0	0	0	1	2	2
7	8	6	4	5	6	1	0	0	1	1	2
8	8	4	4	5	5	2	1	0	1	2	1
6	9	2	5	5	4	2	1	0	0	1	1
5	7	4	3	4	3	1	2	0	0	1	2
8	8	4	6	4	6	0	1	1	0	2	2
3	8	3	2	3	5	1	1	0	0	0	0
8	8	7	7	6	6	2	2	1	2	2	2
9	3	5	2	5	4	2	0	1	0	0	1
8	9	5	10	1	6	0	1	0	2	2	2
7	8	4	4	5	6	3	3	0	0	1	2
3	8	2	6	2	6	1	2	0	1	0	0
7	8	8	7	5	6	0	2	1	1	0	2
6	9	3	2	6	4	3	2	1	0	0	2
8	7	4	6	4	6	0	3	1	0	0	2
7	7	2	3	4	6	2	3	1	1	0	2
6	7	5	2	6	4	0	0	1	1	1	1
8	8	8	5	5	5	1	2	1	1	0	2
8	7	4	6	4	6	2	2	0	1	2	1
8	8	6	4	5	6	1	2	1	1	0	1
7	8	3	6	4	6	0	1	0	1	1	0
9	9	4	4	2	6	0	3	1	0	2	0
8	6	4	3	6	5	1	2	0	0	2	1
4	8	3	2	5	3	0	0	0	0	2	0
8	8	6	3	3	5	1	0	2	1	0	2
6	7	3	7	3	5	0	3	0	1	2	0
7	8	5	4	5	6	2	2	1	2	2	2
3	7	3	3	2	6	2	0	0	0	0	0
8	7	4	2	5	4	3	2	0	2	1	2
8	9	6	5	6	6	2	0	1	0	1	1
5	7	6	4	5	6	0	0	0	1	0	2
7	9	5	6	6	6	1	2	1	2	2	2
8	6	6	4	4	2	0	0	0	0	0	2
7	9	3	9	5	6	3	2	0	1	2	1
6	9	3	8	1	6	0	2	1	2	2	0
2	9	4	2	1	5	0	2	1	0	1	0
4	8	5	2	3	5	3	3	0	0	1	0
8	9	3	1	3	5	2	2	0	0	0	2
6	5	5	4	6	4	0	3	0	1	1	1
8	8	4	5	4	5	2	2	0	1	2	1
7	9	10	6	6	6	3	2	2	1	2	2
8	8	3	8	6	6	2	3	2	1	1	2
7	9	4	6	6	5	0	2	0	1	1	1
6	7	3	8	5	5	2	0	0	2	2	2
9	7	4	4	6	6	0	1	0	0	1	0
9	8	4	7	5	6	2	1	0	0	2	2
7	8	3	2	4	4	0	1	0	1	0	0
7	6	6	6	6	5	0	2	2	1	2	2
9	6	4	4	4	6	2	1	0	1	0	1
7	7	2	6	4	5	0	0	0	0	2	0
6	7	4	5	5	5	0	0	1	0	0	0
3	9	2	8	1	5	0	1	1	1	2	0
8	8	3	5	5	5	0	2	0	2	2	1
8	8	4	6	5	5	0	2	0	1	2	2
7	7	6	4	5	5	2	2	1	1	2	2
7	9	9	3	6	4	2	2	1	1	0	2
6	6	4	2	5	0	2	0	1	0	2	0
8	7	8	4	6	5	3	0	1	0	1	2
7	8	4	5	5	6	3	0	0	0	2	0
8	6	7	4	6	5	3	2	2	0	0	1
9	7	8	6	6	6	2	0	1	2	0	2
9	7	3	3	6	6	2	2	0	2	1	1
6	7	5	4	6	6	2	1	1	2	2	2
9	9	4	3	5	6	1	3	0	2	0	1
8	6	3	4	3	6	0	0	0	0	2	2
6	8	5	8	6	5	1	3	0	1	1	2
9	9	5	5	4	5	2	2	1	0	2	2
8	8	4	5	6	5	0	3	0	0	0	0
8	6	5	3	3	6	1	2	0	0	1	1
9	6	7	3	4	4	3	0	0	0	1	2
9	9	7	4	4	5	3	0	0	0	1	2
9	6	7	3	4	6	3	0	0	0	1	2
8	9	7	6	4	6	3	1	0	1	1	2
5	8	3	6	2	5	1	2	0	2	2	2
4	8	6	4	5	6	1	3	0	2	1	1
8	6	8	4	6	6	1	2	1	0	2	1
3	4	0	3	1	4	0	2	0	1	2	0
6	8	3	2	4	6	1	1	0	2	0	2
6	5	6	5	5	5	1	2	1	0	0	2
6	7	3	4	5	6	0	2	1	2	2	2
9	9	3	4	6	6	2	2	0	0	2	0
8	8	4	3	5	4	3	2	0	1	2	2
0	6	0	4	0	5	0	2	0	2	1	0
6	8	1	2	2	6	0	1	1	1	1	2
8	3	3	0	5	0	0	0	0	0	1	2
8	8	7	4	5	6	2	2	0	1	1	2
6	4	3	4	4	6	1	0	0	0	0	0
9	8	9	4	6	5	3	3	0	2	1	2
6	9	4	6	5	5	0	2	1	2	1	2
4	8	3	6	6	5	0	3	1	0	2	1
5	9	3	5	6	6	3	2	1	2	2	1
8	8	6	4	5	5	2	0	0	1	2	1
6	9	2	6	6	6	1	3	0	2	2	1
7	6	4	5	5	5	2	3	0	0	2	1
4	8	0	4	0	6	0	2	0	2	2	0
8	7	2	4	6	6	1	1	1	2	2	2
8	9	6	4	5	6	1	3	0	2	1	2
8	8	5	8	6	6	2	2	1	2	1	2
7	9	4	10	4	6	0	3	0	1	2	2
8	8	7	5	6	6	3	2	2	2	1	1
6	8	4	5	5	6	1	2	0	2	0	1
8	8	5	3	6	6	0	1	1	0	2	2
9	8	3	3	4	5	2	1	0	1	1	1
7	9	3	4	2	4	1	1	0	1	2	2
8	8	7	7	6	6	3	3	0	1	1	2
9	8	5	4	6	4	1	1	1	2	1	0
6	8	5	7	5	4	1	0	0	0	2	1
9	8	7	7	6	6	3	2	0	1	2	2
5	3	4	1	5	4	2	1	1	1	0	0
8	6	5	2	4	4	2	2	0	1	2	0
4	9	4	8	3	6	0	3	0	2	2	2
7	9	4	5	5	5	0	2	0	0	2	0
1	7	0	7	1	6	0	1	0	0	1	0
8	7	7	4	6	6	2	2	1	0	2	2
9	8	5	1	4	5	3	0	0	0	2	2
8	8	6	5	5	6	2	2	0	0	1	1
8	7	5	3	6	4	3	1	0	0	1	1
8	6	4	4	6	6	3	1	1	1	1	2
4	9	1	6	2	5	0	2	0	2	2	0
5	9	6	4	5	6	1	1	0	1	2	2
6	5	5	3	4	1	3	0	0	0	2	0
8	6	5	5	5	5	2	1	0	1	2	1
4	9	5	3	4	5	2	2	2	2	1	2
5	9	3	6	6	4	0	2	0	1	2	2
5	8	5	9	5	6	2	2	0	2	2	2
7	4	7	5	6	6	3	0	0	1	2	1
8	8	5	4	2	5	1	3	0	1	0	0
8	9	1	5	4	5	0	2	1	1	1	2
9	8	5	5	6	6	3	2	1	1	1	2
6	8	4	3	5	6	2	2	0	0	2	1
8	4	5	0	3	0	2	0	0	0	2	1
5	9	5	5	6	5	2	3	1	0	2	0
3	6	5	3	4	5	1	0	1	1	1	2
6	3	2	0	6	0	2	0	0	0	2	0
4	7	4	3	4	4	2	0	0	1	1	2
6	7	6	5	6	5	1	2	0	0	2	1
7	8	5	4	0	5	1	3	0	1	2	2
6	7	6	8	4	6	3	2	2	1	2	1
9	7	7	6	5	6	2	1	0	1	2	1
6	8	4	5	4	5	2	1	0	1	2	0
4	7	2	3	2	6	2	1	0	0	2	1
5	9	6	6	2	6	1	0	2	1	1	1
5	7	5	6	3	5	0	3	1	1	1	2
6	8	3	7	6	6	3	2	1	2	2	1
5	6	4	6	3	4	2	3	0	1	2	1
7	5	5	4	6	4	2	0	0	0	1	1
6	7	4	4	5	6	1	1	0	2	2	0
6	6	3	2	5	1	3	0	0	1	0	1
8	7	5	5	6	5	3	2	1	0	2	2
9	9	3	2	5	6	3	2	0	2	2	1
4	9	4	7	5	4	1	3	0	2	0	2
7	8	6	6	6	5	2	2	0	2	1	1
6	8	3	5	5	6	2	2	0	2	1	1
7	8	5	4	6	4	3	2	1	0	2	0
8	9	6	8	6	3	2	2	0	1	0	1
5	6	6	5	3	6	1	2	0	1	2	0
5	7	3	5	4	2	1	2	0	0	1	0
8	8	6	5	5	6	2	3	0	0	2	0
5	9	6	8	6	5	0	3	1	2	0	0
7	9	4	7	6	6	2	3	2	2	2	1
6	7	4	3	5	6	1	3	1	0	1	2
5	2	5	5	5	6	0	3	0	2	2	0
9	8	6	7	5	6	1	3	0	2	2	2
7	9	4	7	5	5	2	2	0	2	2	2
9	7	4	3	5	6	2	1	0	1	1	2
7	7	4	5	6	6	1	2	1	2	2	1
5	8	4	5	4	5	2	3	0	2	1	0
6	7	6	4	4	6	2	3	0	0	2	2
8	6	8	3	6	5	3	1	0	0	0	2
7	7	6	5	4	5	1	3	0	0	2	2
4	8	4	7	4	6	0	3	1	2	2	0
6	7	4	5	6	6	2	2	1	2	2	2
8	8	9	9	5	6	3	3	0	2	2	1
7	5	6	4	6	6	2	2	1	0	1	2
9	9	4	4	4	5	0	3	0	1	0	1
4	5	3	3	6	4	1	1	1	0	0	1
7	7	4	4	6	5	1	3	1	0	0	1
6	8	5	4	6	5	3	3	0	2	2	2
8	5	4	5	4	4	2	2	0	2	0	2
8	4	8	2	5	5	3	3	0	1	2	2
8	4	5	3	6	6	3	1	1	0	2	2
7	4	4	8	4	4	1	3	0	0	2	2
6	6	5	4	4	5	0	3	1	1	1	0
8	5	4	6	5	2	2	3	2	0	1	2
4	7	2	5	5	5	1	2	0	1	2	2
9	7	7	5	3	5	3	1	0	0	2	2
4	7	2	7	5	5	1	2	0	2	1	1
8	8	3	9	6	6	3	3	2	0	0	1
6	8	7	8	4	5	3	3	1	1	1	1
5	7	4	4	1	4	0	3	0	0	2	0
9	9	7	7	6	5	3	3	0	1	2	1
6	5	9	7	4	6	2	3	0	1	0	2
9	7	7	4	2	5	3	1	1	1	2	1
6	6	5	5	6	4	1	1	0	1	2	1
6	8	4	9	5	1	1	2	0	0	2	1
7	7	6	4	2	4	1	1	0	0	0	0
2	7	3	5	4	6	0	3	0	1	2	2
6	7	3	6	6	4	2	3	0	2	1	2
9	6	5	3	6	5	2	3	2	0	2	2
8	8	5	8	3	6	3	3	0	1	1	1
9	8	7	4	4	4	3	2	0	0	0	1
6	6	9	3	6	5	3	2	0	2	2	1
8	7	5	6	3	6	3	3	0	0	1	1
7	8	5	4	4	6	1	3	0	2	0	2
7	8	1	5	4	5	2	2	0	0	0	2
6	7	6	4	6	6	3	3	0	1	1	1
8	6	5	5	4	6	2	1	0	0	2	0
5	8	4	6	2	6	2	2	0	1	2	2
4	7	2	4	4	6	0	1	0	1	2	2
5	8	5	6	1	6	2	2	1	2	0	1
8	7	3	8	6	6	3	2	2	2	2	0
6	9	5	4	4	0	3	3	0	0	2	0
6	7	4	7	4	6	2	2	0	2	1	2
6	8	6	7	0	5	0	2	0	1	2	0
7	9	7	6	4	6	3	3	2	1	2	1
7	6	5	3	2	5	1	0	0	0	2	2
7	8	5	2	6	2	2	0	1	1	2	1
7	9	2	6	3	6	2	3	0	1	0	1
8	5	3	5	5	6	3	0	0	1	1	1
7	8	5	3	6	6	3	1	1	1	2	2
7	6	2	5	4	6	1	2	0	0	2	1
7	6	2	5	6	4	3	0	0	1	0	2
7	5	5	4	3	5	3	2	1	0	2	1
5	8	2	8	5	6	3	2	0	0	2	2
4	9	3	3	3	6	0	3	0	1	1	1
6	7	4	4	5	6	3	1	1	0	2	2
9	5	8	3	5	6	3	2	2	0	1	1
4	8	4	5	4	5	0	3	0	0	1	2
4	7	2	5	5	5	1	2	0	1	1	1
8	9	5	6	5	4	3	2	0	0	2	0
3	6	4	4	5	5	0	1	0	0	2	1
8	7	5	6	4	5	0	3	0	2	2	2
8	6	8	4	5	3	3	0	1	1	2	2
8	9	6	7	5	5	3	1	1	0	1	2
8	6	9	5	6	1	3	1	1	0	2	0
8	9	2	5	4	6	1	3	0	1	1	1
7	8	3	6	5	6	3	1	1	1	1	1
5	6	6	4	4	6	2	3	0	1	1	1
7	7	2	4	6	1	3	2	0	0	1	1
7	9	3	2	4	5	2	3	0	1	1	1
4	6	1	6	4	3	0	3	0	0	2	1
6	5	2	3	2	4	0	2	0	2	1	1
6	6	1	4	4	5	1	2	0	1	1	1
6	8	2	3	4	4	0	0	0	0	1	2
4	3	5	4	3	4	0	0	0	0	0	0
5	6	6	8	2	5	1	1	1	0	1	0
8	8	2	5	4	5	1	1	0	0	0	0
7	9	2	5	4	6	1	2	0	2	2	0
6	7	4	3	3	5	0	3	0	0	1	1
4	5	5	5	1	3	0	1	0	0	0	0
4	7	2	5	5	5	1	2	0	2	1	1
4	9	1	5	4	5	1	2	0	2	1	1
8	9	1	5	5	6	0	3	0	2	2	0
6	8	5	5	4	6	3	3	1	0	1	1
4	8	0	4	3	2	0	2	0	2	1	0
8	9	5	5	6	5	3	3	0	1	2	2
4	8	3	4	5	4	3	3	0	1	1	1
5	9	7	8	5	6	3	3	0	1	2	2
9	7	6	2	5	5	3	2	0	0	2	2
2	8	2	5	1	6	0	2	0	0	2	0
6	7	5	7	4	5	2	3	0	2	2	2
5	5	2	3	3	6	0	3	0	1	0	0
3	7	2	5	5	6	1	3	1	1	2	0
8	6	6	4	5	6	2	3	0	1	0	1
8	9	3	5	5	4	3	2	0	1	1	1
4	6	3	5	4	5	3	0	1	1	2	2
5	7	1	4	6	5	0	2	0	0	2	1
3	4	1	4	4	4	0	2	0	0	2	1
5	6	3	5	4	5	0	2	0	0	1	0
8	7	1	4	4	6	0	3	0	0	1	1
4	9	4	4	5	6	1	1	0	0	1	0
9	6	3	5	2	6	1	2	0	1	2	2
4	8	2	7	2	5	0	3	1	2	1	0
3	9	4	7	5	6	1	3	0	1	2	1
8	7	3	5	6	6	2	3	0	0	1	2
6	8	1	4	5	6	1	1	0	0	2	0
9	8	3	6	5	6	1	3	1	1	1	1
8	9	1	6	6	6	1	0	0	1	2	0
8	7	9	7	6	6	3	3	0	2	1	2
4	9	3	5	2	5	2	2	0	2	1	1
8	7	4	4	5	4	1	2	0	1	2	2
8	8	1	6	4	4	2	3	1	1	2	2
8	4	4	5	6	5	2	2	0	1	2	1
7	3	5	3	6	5	3	0	1	0	1	1
5	9	3	5	3	6	1	2	0	0	2	1
9	7	3	5	6	6	3	1	0	1	2	1
6	6	2	1	4	5	0	3	0	1	2	2
9	3	5	6	6	6	2	2	0	2	2	1
7	8	2	1	5	6	0	3	0	1	1	2
8	7	2	4	4	5	0	3	0	1	2	0
8	6	5	6	6	6	3	3	0	1	2	0
6	4	2	3	5	4	3	0	1	1	2	2
8	6	5	6	6	5	2	3	0	0	0	2
7	7	4	4	2	5	1	3	0	1	1	2
8	9	4	3	6	6	0	3	0	0	0	2
5	5	0	2	3	6	0	1	0	0	2	0
6	9	0	5	4	6	0	2	1	1	1	0
6	5	3	2	5	2	1	0	0	0	2	1
5	6	1	3	3	5	0	0	0	0	2	1
6	4	2	4	6	4	1	1	0	1	2	2
6	3	2	3	4	6	2	2	0	1	2	2
4	9	2	2	3	6	0	1	0	0	2	1
8	8	1	6	3	6	3	3	0	1	0	0
5	6	4	2	3	6	0	1	0	1	0	1
6	8	5	3	5	4	2	1	0	0	1	1
5	6	1	5	5	4	1	2	0	0	2	2
5	4	4	1	5	1	3	0	0	0	2	1
9	7	7	4	4	4	3	2	1	0	1	1
5	6	1	2	2	3	0	0	0	0	1	2
7	9	4	8	6	6	3	3	1	0	1	1
7	8	3	6	4	6	0	3	0	0	2	1
7	8	4	5	4	5	3	3	0	0	0	0
9	6	4	3	5	6	3	1	0	1	1	1
7	8	3	5	3	6	0	2	1	1	1	1
4	8	4	3	6	5	3	1	2	1	0	1
7	7	3	4	5	5	1	2	1	0	2	1
7	9	4	6	3	6	2	3	0	1	1	1
3	7	5	5	3	4	0	2	0	0	2	1
5	8	2	1	3	6	1	3	0	1	2	1
8	6	5	3	4	3	3	1	1	0	1	2
9	6	7	2	6	4	3	1	0	0	2	1
9	8	8	8	6	5	3	3	1	1	2	1
6	NA	4	NA	5	NA	1	NA	1	NA	2	NA
7	NA	5	NA	2	NA	2	NA	0	NA	1	NA
2	NA	1	NA	0	NA	0	NA	0	NA	2	NA
5	NA	2	NA	3	NA	0	NA	1	NA	1	NA
6	NA	0	NA	5	NA	3	NA	0	NA	2	NA
7	NA	6	NA	5	NA	3	NA	1	NA	0	NA
6	NA	1	NA	4	NA	1	NA	0	NA	1	NA
8	NA	4	NA	5	NA	2	NA	0	NA	1	NA
9	NA	4	NA	6	NA	3	NA	1	NA	2	NA
3	NA	4	NA	5	NA	1	NA	0	NA	1	NA
9	NA	3	NA	6	NA	3	NA	1	NA	2	NA
8	NA	2	NA	1	NA	0	NA	0	NA	2	NA
6	NA	1	NA	4	NA	1	NA	0	NA	2	NA
6	NA	3	NA	4	NA	0	NA	0	NA	2	NA
8	NA	6	NA	1	NA	2	NA	0	NA	2	NA
5	NA	2	NA	2	NA	0	NA	1	NA	2	NA
6	NA	1	NA	5	NA	1	NA	1	NA	2	NA
7	NA	4	NA	6	NA	3	NA	1	NA	2	NA
4	NA	4	NA	4	NA	3	NA	0	NA	2	NA
7	NA	2	NA	5	NA	0	NA	1	NA	1	NA
4	NA	0	NA	1	NA	0	NA	0	NA	1	NA
6	NA	4	NA	5	NA	3	NA	1	NA	1	NA
8	NA	5	NA	5	NA	2	NA	2	NA	2	NA
7	NA	6	NA	3	NA	3	NA	0	NA	2	NA
5	NA	2	NA	3	NA	1	NA	0	NA	2	NA
8	NA	2	NA	5	NA	1	NA	2	NA	2	NA
9	NA	4	NA	6	NA	3	NA	0	NA	2	NA
4	NA	0	NA	0	NA	0	NA	0	NA	2	NA
7	NA	5	NA	6	NA	0	NA	1	NA	1	NA
7	NA	7	NA	5	NA	3	NA	0	NA	2	NA
9	NA	6	NA	5	NA	3	NA	1	NA	0	NA
7	NA	5	NA	5	NA	3	NA	0	NA	1	NA
5	NA	1	NA	2	NA	0	NA	1	NA	1	NA
8	NA	4	NA	5	NA	2	NA	1	NA	2	NA
7	NA	3	NA	5	NA	1	NA	0	NA	0	NA
5	NA	3	NA	5	NA	3	NA	0	NA	0	NA
8	NA	7	NA	3	NA	0	NA	0	NA	2	NA
9	NA	3	NA	4	NA	3	NA	0	NA	2	NA
8	NA	3	NA	6	NA	3	NA	1	NA	1	NA
6	NA	5	NA	3	NA	1	NA	0	NA	1	NA
2	NA	3	NA	2	NA	2	NA	0	NA	1	NA
9	NA	7	NA	5	NA	3	NA	2	NA	0	NA
4	NA	3	NA	3	NA	0	NA	1	NA	1	NA
7	NA	2	NA	5	NA	1	NA	0	NA	1	NA
9	NA	6	NA	5	NA	1	NA	0	NA	2	NA
5	NA	2	NA	3	NA	0	NA	0	NA	1	NA
3	NA	3	NA	4	NA	2	NA	0	NA	1	NA
4	NA	1	NA	3	NA	0	NA	0	NA	1	NA
8	NA	3	NA	6	NA	3	NA	1	NA	0	NA
6	NA	7	NA	5	NA	1	NA	1	NA	0	NA
5	NA	3	NA	4	NA	1	NA	0	NA	2	NA
7	NA	4	NA	4	NA	0	NA	0	NA	0	NA
7	NA	1	NA	5	NA	1	NA	0	NA	1	NA
5	NA	2	NA	6	NA	0	NA	0	NA	1	NA
7	NA	2	NA	5	NA	3	NA	0	NA	0	NA
8	NA	7	NA	4	NA	3	NA	2	NA	2	NA
6	NA	1	NA	5	NA	0	NA	0	NA	1	NA
6	NA	1	NA	3	NA	1	NA	1	NA	2	NA
6	NA	4	NA	5	NA	3	NA	0	NA	2	NA
8	NA	2	NA	5	NA	0	NA	0	NA	0	NA
8	NA	3	NA	5	NA	2	NA	0	NA	1	NA
7	NA	3	NA	4	NA	0	NA	0	NA	0	NA
6	NA	3	NA	5	NA	2	NA	1	NA	1	NA
3	NA	3	NA	6	NA	0	NA	0	NA	1	NA
8	NA	2	NA	5	NA	2	NA	1	NA	1	NA
8	NA	4	NA	5	NA	0	NA	1	NA	0	NA
8	NA	5	NA	6	NA	3	NA	1	NA	2	NA
7	NA	5	NA	4	NA	3	NA	0	NA	2	NA
5	NA	2	NA	4	NA	3	NA	1	NA	2	NA
5	NA	2	NA	3	NA	0	NA	1	NA	1	NA

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'Sir Maurice George Kendall' @ kendall.wessa.net
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.
R Engine error message	Error in if (gqarr[mypoint - kp3 + 1, 2] < 0.01) numsignificant1 <- numsignificant1 + : missing value where TRUE/FALSE needed Execution halted

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
R Framework error message & The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'. \tabularnewline
R Engine error message & Error in if (gqarr[mypoint - kp3 + 1, 2] < 0.01) numsignificant1 <- numsignificant1 +  : 
  missing value where TRUE/FALSE needed
Execution halted
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268737&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.[/C][/ROW]
[ROW][C]R Engine error message[/C][C]Error in if (gqarr[mypoint - kp3 + 1, 2] < 0.01) numsignificant1 <- numsignificant1 +  : 
  missing value where TRUE/FALSE needed
Execution halted
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268737&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268737&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	6 seconds
R Server	'Sir Maurice George Kendall' @ kendall.wessa.net
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.
R Engine error message	Error in if (gqarr[mypoint - kp3 + 1, 2] < 0.01) numsignificant1 <- numsignificant1 + : missing value where TRUE/FALSE needed Execution halted

Parameters (Session):

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$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$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1/numgqtests,6))
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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code