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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, 20 Nov 2017 14:22:09 +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/Nov/20/t1511184238mk8irtq7avmpp9e.htm/, Retrieved Fri, 17 May 2024 22:36:33 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=308134, Retrieved Fri, 17 May 2024 22:36:33 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

134

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

-       [Multiple Regression] [Multiple regression] [2017-11-20 13:22:09] [b9ba5da1e46a180616c603fd7f584a37] [Current]

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Dataseries X:

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Histogram

Boxplots

4	4	4	4	5	4	4	4	3	5	2	4	4	4	2	2	4	4	4	1
4	4	4	4	4	3	4	5	2	4	1	5	4	3	3	4	5	4	2	3
2	4	4	4	3	3	4	3	3	2	3	4	4	4	3	3	4	4	2	2
4	4	4	3	4	2	2	3	2	4	3	4	5	4	4	2	3	2	4	2
4	4	2	3	4	4	4	4	2	3	1	4	4	3	2	3	3	4	4	1
3	3	3	3	2	3	3	2	1	3	3	4	3	2	1	4	3	3	3	2
2	4	4	5	5	5	5	5	4	5	2	4	5	4	5	1	4	4	4	2
5	4	5	4	5	3	5	3	4	4	5	4	5	5	4	2	3	3	4	3
4	4	4	2	4	4	4	5	3	4	3	5	5	4	4	2	3	4	4	3
3	4	4	4	4	4	2	3	3	3	2	4	5	2	3	2	3	3	4	2
4	3	3	4	5	3	4	4	3	4	1	5	3	3	3	2	3	3	4	3
3	3	4	4	3	2	4	4	2	5	4	4	5	4	4	4	4	4	4	3
3	5	4	4	5	5	3	5	2	4	3	4	4	4	3	2	2	3	2	2
3	4	4	4	4	4	3	4	3	4	4	4	2	2	3	3	4	3	4	3
4	3	3	3	2	4	4	2	4	3	3	3	3	3	4	3	2	3	3	4
4	4	2	2	2	5	4	5	4	5	3	4	4	3	4	1	3	4	3	2
3	5	4	4	3	4	2	3	4	3	3	3	4	4	4	3	4	3	3	4
2	4	2	4	3	2	2	4	2	4	1	3	5	2	2	1	1	1	3	1
3	5	4	4	4	3	5	4	3	4	5	4	4	5	2	2	4	5	2	2
3	4	2	3	4	5	4	4	3	3	4	5	2	4	4	3	5	4	4	2
3	4	2	4	4	3	3	4	3	4	2	3	4	4	2	2	4	2	3	3
3	4	4	2	4	3	3	5	3	3	2	4	1	5	3	2	4	3	2	4
2	2	2	4	2	2	3	2	3	2	4	2	4	2	4	4	2	3	2	2
4	3	4	4	3	4	4	4	4	4	3	4	4	3	4	3	3	3	3	3
5	5	4	4	4	4	4	5	3	4	2	4	2	3	2	1	2	2	2	1
4	4	4	3	4	4	4	4	3	4	2	4	5	3	2	2	4	2	4	3
4	4	2	2	2	2	2	2	4	2	4	2	2	3	2	4	2	2	4	4
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
2	5	4	3	3	4	2	4	3	4	5	4	5	4	4	2	4	5	2	1
4	4	3	3	4	4	4	4	3	4	2	3	4	3	4	2	4	4	4	2
4	4	4	3	2	4	5	5	4	4	5	1	4	3	3	2	5	3	3	1
4	4	5	2	2	4	3	4	4	4	4	3	4	3	4	3	4	5	3	3
4	5	4	3	4	4	4	4	3	4	4	4	5	3	4	2	4	3	4	2
2	4	2	3	3	4	2	4	3	2	4	3	4	3	2	2	4	4	3	3
2	3	2	3	4	4	4	4	3	4	2	3	4	5	2	3	3	4	3	2
4	2	4	4	3	4	4	4	3	4	3	4	4	3	4	1	2	3	4	2
4	2	4	4	3	4	2	4	1	4	3	4	2	3	4	1	5	4	5	5
3	3	4	3	3	4	3	4	3	4	3	4	4	4	3	2	4	4	4	4
4	2	3	3	5	4	2	4	3	3	2	5	5	5	4	2	4	4	4	4
4	4	4	3	4	4	4	3	2	4	3	4	4	4	3	3	5	4	3	3
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
4	4	5	4	5	2	4	4	4	4	2	4	5	2	5	3	4	4	2	3
3	4	4	4	3	4	3	4	3	4	4	4	5	4	3	2	4	4	3	3
5	5	5	2	4	3	3	4	2	2	2	4	5	3	4	3	3	4	4	3
3	4	3	4	4	3	4	4	3	4	2	4	5	3	3	3	3	4	4	3
3	4	4	3	4	4	3	4	4	4	2	4	4	3	3	2	3	3	3	2
2	3	3	3	2	4	2	3	2	3	3	2	4	2	3	4	2	1	4	2
5	5	4	4	4	4	4	5	3	5	5	3	4	4	4	1	3	2	3	4
3	4	3	4	4	3	4	3	2	2	4	4	5	4	3	3	3	4	4	2
3	4	3	3	5	4	3	4	3	4	2	3	5	4	3	2	4	4	4	3
4	3	3	4	5	4	4	4	3	4	2	3	4	4	5	2	3	4	4	2
3	4	5	5	5	4	5	5	3	5	4	4	5	5	3	3	3	4	3	3
3	4	3	3	4	3	4	3	3	4	3	3	5	3	3	2	3	2	3	2
2	4	3	2	5	1	3	4	2	5	1	5	4	2	3	2	4	4	4	2
3	3	1	3	3	4	2	4	2	3	2	5	5	3	4	5	4	4	5	4
4	4	4	4	4	4	4	4	3	4	2	4	2	4	2	3	5	4	3	4
2	3	4	5	4	4	4	5	2	5	2	4	4	4	4	3	4	4	2	3
4	4	5	4	4	4	4	3	3	4	3	4	4	2	4	2	4	5	5	4
3	5	4	2	4	4	4	4	3	4	4	3	5	3	3	2	3	4	4	2
4	4	5	1	3	5	5	5	2	5	5	4	4	5	4	3	2	4	5	3
4	5	4	3	4	4	2	3	2	3	1	4	4	2	3	3	4	4	2	3
4	4	4	3	4	4	4	5	2	4	2	4	5	3	4	4	4	4	3	3
2	3	4	3	4	3	3	3	2	2	2	4	4	4	2	4	3	4	2	1
2	4	4	4	5	5	4	5	4	5	2	2	4	3	2	4	5	2	4	3
2	4	2	2	4	1	2	2	1	2	1	4	2	4	2	2	4	4	4	2
4	4	4	4	4	5	4	4	3	4	2	5	5	4	5	5	4	5	5	5
4	2	3	3	4	3	3	5	2	4	3	4	4	1	2	2	4	2	2	2
2	3	4	4	4	4	4	5	3	4	3	4	4	3	4	2	3	3	5	2
4	4	3	4	4	5	4	4	3	4	2	4	4	4	2	3	5	4	3	2
4	4	5	5	4	4	5	5	3	4	4	4	3	4	4	2	4	4	4	3
4	3	4	2	3	5	5	5	4	5	3	5	2	3	3	3	4	4	3	3
3	4	3	2	2	4	3	5	4	3	5	3	5	5	1	1	3	4	3	4
4	5	5	4	5	5	4	5	3	5	1	5	4	4	4	3	4	4	3	4
4	3	5	3	4	4	4	2	4	3	2	4	4	4	4	2	4	2	4	2
5	4	5	3	4	5	4	4	2	5	3	5	4	3	4	2	4	4	4	3
4	4	4	2	4	4	4	5	1	5	4	4	5	4	1	2	4	4	1	4
3	2	2	3	4	3	4	3	2	4	2	4	3	4	3	2	4	4	4	2
4	4	4	4	4	4	4	5	3	4	3	5	5	3	3	3	4	3	3	3
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
2	5	4	1	5	5	5	5	4	4	2	4	2	4	2	2	2	4	4	1
2	4	2	2	2	4	4	5	2	5	2	5	4	5	2	4	4	4	3	2
4	3	4	4	4	4	3	4	2	4	3	4	5	3	3	1	3	4	2	2
4	4	4	2	4	4	4	5	1	4	2	3	5	4	3	1	3	4	4	3
4	4	4	4	4	4	4	4	1	4	4	5	5	4	4	2	4	4	4	4
2	4	3	5	5	2	3	3	1	3	3	4	4	2	4	1	3	4	4	3
5	5	5	5	5	5	5	5	3	5	1	4	4	4	4	2	3	3	4	1
4	4	4	4	4	3	4	4	3	5	1	4	5	3	3	4	3	3	3	3
2	3	2	2	4	2	4	2	1	2	3	4	4	2	3	4	3	4	4	4
5	5	3	4	4	4	4	5	3	5	2	5	4	3	4	3	3	3	4	3
3	3	3	3	4	4	4	4	3	5	3	4	4	5	3	3	3	3	3	3
4	5	4	4	4	4	3	4	3	4	3	3	5	2	3	1	3	3	4	4
5	4	5	4	4	3	4	5	4	5	2	5	5	5	3	2	3	4	4	5
3	4	4	4	4	4	2	4	2	3	1	3	4	4	2	2	3	4	4	2
3	3	4	4	4	3	5	4	2	4	3	4	4	3	3	2	3	3	3	4
4	5	5	4	4	4	4	4	3	5	1	4	4	2	4	2	3	5	4	2
4	5	4	5	5	4	5	5	4	5	2	5	5	4	4	3	4	2	5	4
4	4	4	4	3	5	3	4	3	4	2	4	5	4	3	4	4	4	4	2
3	4	5	3	4	5	3	4	2	3	4	2	5	3	4	2	3	2	5	1
3	2	2	4	3	3	4	4	2	2	3	4	4	3	3	2	3	3	3	3
4	4	3	4	2	5	2	3	2	3	5	3	5	4	1	1	3	3	3	1
5	5	3	4	3	5	4	5	4	5	5	4	4	5	5	3	3	4	5	4
3	4	4	4	5	4	3	5	3	3	4	4	4	4	4	3	3	3	3	3
5	5	5	4	5	5	5	5	3	5	3	5	5	4	4	4	3	5	4	3
4	4	2	3	4	4	3	4	2	4	2	4	4	3	3	2	4	3	3	2
4	5	4	3	3	4	3	3	4	4	3	4	4	3	4	2	3	4	4	2
4	4	4	4	4	4	3	3	2	4	2	4	4	3	3	3	3	2	4	1
4	4	3	3	3	4	4	4	4	4	4	4	4	3	4	4	2	4	3	4
1	5	5	5	4	5	2	4	1	4	3	5	5	3	4	4	1	3	2	1
3	3	2	4	4	4	4	4	4	4	3	3	4	3	3	2	3	4	3	2
4	4	4	4	4	3	4	4	3	4	2	4	2	4	4	2	4	4	4	3
4	4	2	3	5	5	4	5	3	4	3	5	5	5	3	2	2	2	3	4
4	4	4	4	4	3	4	4	2	4	4	2	4	3	3	2	2	4	3	3
4	4	2	3	3	5	2	4	3	4	4	4	4	2	2	1	4	5	3	2
3	4	3	3	4	4	3	4	3	4	3	5	5	4	5	4	4	4	4	4
4	4	3	4	3	4	4	4	4	4	3	4	4	3	3	3	4	4	4	3
2	2	4	2	3	4	5	5	1	5	2	4	4	5	3	1	4	1	4	1
2	2	4	3	4	5	4	5	4	5	5	4	2	4	3	3	3	4	3	3
3	4	5	4	4	4	4	4	4	5	3	3	5	3	3	3	4	3	3	1
5	4	4	5	3	4	4	4	3	5	1	4	5	5	2	2	4	4	4	4
3	3	4	4	4	5	4	4	4	4	1	4	5	3	3	1	4	4	4	1
4	4	4	2	5	4	4	4	2	5	4	4	2	5	2	2	4	4	5	4
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
3	3	2	4	4	3	3	4	4	4	3	4	4	4	3	3	3	3	2	2
2	1	4	3	3	5	2	4	3	2	5	4	4	5	2	3	4	5	3	2
3	3	3	3	3	5	3	4	3	4	3	3	4	4	3	1	4	3	4	2
2	3	3	3	3	5	4	4	3	4	2	5	5	3	3	3	4	4	3	3
5	5	5	4	4	3	5	5	4	5	4	4	5	4	4	4	5	5	3	5
4	5	1	3	5	4	4	3	3	4	1	3	5	4	4	1	2	3	4	4
3	4	4	3	4	4	3	4	3	4	3	4	2	3	3	4	3	4	3	3
4	3	4	4	3	5	4	5	2	4	1	4	2	5	3	2	4	5	4	2
2	3	4	3	3	4	3	4	4	5	1	4	2	3	4	2	3	4	4	2
3	3	4	4	4	3	4	4	2	2	2	2	2	2	2	2	3	2	3	1
4	4	4	5	3	4	3	4	3	5	2	4	5	5	4	4	4	3	3	3
2	4	4	2	3	4	2	4	4	3	2	4	5	4	4	3	4	4	4	5
4	3	5	4	4	3	5	5	3	4	3	5	5	4	3	5	3	3	2	3
5	5	4	2	3	5	5	5	4	4	5	5	2	5	4	4	4	4	2	4
4	4	4	4	4	3	3	4	4	4	1	2	2	2	3	2	2	4	2	2
3	2	3	2	3	2	5	4	2	4	1	4	5	3	3	2	4	4	4	4
2	3	4	2	4	4	5	4	2	4	3	5	5	4	4	2	4	4	4	4
4	2	4	4	5	5	5	4	2	3	2	4	5	5	3	1	3	5	4	2
4	4	4	4	4	2	4	4	3	4	1	3	5	2	3	2	3	2	3	2
3	3	4	3	5	4	5	4	4	3	2	5	5	4	3	2	4	4	5	4
5	4	3	3	5	4	5	5	3	4	1	5	5	5	4	3	5	5	5	4
2	2	4	3	4	5	5	5	3	4	2	5	5	4	3	1	3	3	4	2
4	3	4	4	3	4	4	5	3	4	4	4	4	4	3	2	3	3	3	3
5	3	2	4	3	4	3	4	4	4	3	4	5	4	3	2	2	4	4	4
3	4	2	4	4	2	4	4	2	4	3	2	5	3	3	1	4	1	4	4
3	4	3	3	4	3	3	4	3	3	1	4	5	3	3	3	4	4	3	3
3	4	3	4	4	3	4	4	3	4	1	4	4	3	3	2	2	4	4	2
3	3	4	4	4	4	3	4	2	4	2	4	4	3	3	1	3	3	4	1
3	3	3	4	3	3	4	3	2	3	4	5	1	4	4	3	4	3	4	3
2	4	4	5	3	4	4	4	3	3	4	5	5	5	3	5	4	5	5	4
4	2	3	3	3	4	4	3	4	3	1	5	4	4	3	1	5	4	4	2
4	4	4	4	5	4	2	3	1	3	1	5	5	3	3	1	4	4	4	2
3	3	3	3	4	4	4	4	3	3	1	4	5	4	3	2	3	4	3	3
4	4	3	2	2	3	4	2	4	2	4	4	2	4	3	4	3	4	4	2
3	4	4	3	4	4	3	4	4	2	2	4	4	4	2	2	3	4	4	2
3	4	4	4	1	3	4	4	5	4	2	3	5	3	3	1	4	2	2	1
2	4	3	4	5	4	2	2	2	4	3	2	5	3	3	1	3	2	4	2
4	2	5	4	4	4	5	5	3	5	3	4	4	3	3	2	2	4	4	4
4	4	4	4	4	4	4	4	5	5	1	4	5	3	3	2	3	3	3	3
4	4	4	3	4	4	5	4	3	3	2	5	5	4	3	1	5	4	5	3
2	2	4	5	4	4	4	5	3	5	1	4	5	2	3	2	2	2	2	2
3	4	4	4	4	4	3	4	3	4	2	3	4	3	2	2	3	3	4	4
2	3	4	2	3	2	4	4	4	5	3	4	1	3	4	1	5	3	2	3
4	1	4	2	2	4	2	4	3	4	5	4	4	2	4	1	4	4	3	3
5	4	5	4	4	4	5	5	3	4	2	4	5	5	4	2	5	3	4	2
4	4	5	3	4	4	4	5	4	4	3	4	5	4	3	2	5	4	5	2
2	4	4	2	4	2	2	4	2	4	4	2	5	2	4	1	2	5	4	2
4	4	3	4	4	4	4	4	3	4	2	3	5	3	3	2	3	3	3	4
4	2	5	4	4	4	4	5	3	5	3	2	4	2	2	3	4	2	3	1
3	4	2	4	4	4	3	4	3	4	2	5	5	4	3	2	3	4	3	3
4	2	4	2	4	3	4	4	4	4	2	3	5	4	4	2	3	2	4	3
4	3	4	3	5	3	4	4	3	4	2	4	5	4	3	4	4	5	4	4
4	4	4	2	2	3	4	4	2	4	4	4	4	4	3	3	2	2	4	4
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
5	3	5	5	5	1	2	4	2	3	4	5	5	5	5	4	4	5	5	4
2	3	3	5	4	4	4	4	1	5	2	4	5	2	3	1	4	4	4	1
4	4	4	3	5	5	5	5	2	5	4	4	5	3	4	1	4	4	4	5
4	4	4	3	5	4	5	5	2	4	2	4	4	4	2	2	5	5	4	3
4	5	2	2	4	4	2	2	5	3	2	5	1	1	3	3	3	1	4	3
3	3	4	4	3	4	5	4	4	3	3	4	5	4	3	4	4	4	3	2
4	4	4	4	4	4	4	4	2	4	1	4	4	4	3	3	4	2	4	4
2	2	3	4	4	2	3	3	1	2	2	4	5	1	2	2	3	3	4	2
4	4	5	4	4	4	4	4	4	4	2	5	4	4	4	2	4	2	2	1
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
4	4	4	4	4	4	4	5	4	5	5	5	5	4	4	2	2	3	5	4
3	4	3	4	4	5	4	5	5	4	4	4	4	4	4	2	4	5	5	4
2	3	3	4	4	4	4	4	1	5	3	4	3	3	5	3	3	4	3	1
4	3	3	4	4	2	4	3	2	3	4	4	2	4	3	2	3	3	3	4
3	3	4	3	4	4	2	3	3	5	2	4	5	4	3	3	3	4	3	4
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
4	3	4	4	4	4	5	5	3	4	1	3	5	3	3	3	3	4	2	4
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
3	3	3	4	3	5	4	4	3	4	2	4	4	5	2	2	2	4	4	4
4	4	2	3	4	4	3	4	4	3	3	5	5	4	3	2	3	4	4	2
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
3	5	3	5	4	4	4	3	2	3	5	4	4	4	4	3	2	5	5	3
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
4	4	2	4	2	2	4	2	4	4	1	4	5	3	2	2	4	4	3	4
4	5	5	5	5	4	5	4	4	5	1	5	5	4	3	2	5	5	2	4
1	4	1	2	2	5	3	4	3	4	4	4	5	4	3	4	4	3	3	3
5	4	5	5	5	4	5	5	3	3	2	4	5	5	4	3	4	4	5	3
4	4	3	4	4	5	4	5	4	2	1	5	5	4	4	4	5	4	4	3
3	3	3	3	4	3	4	4	3	3	1	4	4	4	3	1	3	3	4	2
2	4	2	4	3	4	4	4	3	4	2	3	4	4	3	1	2	2	3	2
4	4	4	4	4	4	5	4	3	5	1	4	4	3	5	1	4	5	3	2
3	4	3	4	3	4	4	4	2	3	1	3	5	4	3	1	2	3	4	3
3	4	4	3	3	4	4	4	4	3	2	3	5	3	3	2	4	4	4	3
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
3	4	3	5	5	4	4	4	3	3	3	4	4	2	3	3	3	3	3	4
3	3	3	4	4	3	3	4	2	4	3	4	5	3	3	2	4	3	4	3
3	4	3	4	5	5	4	4	3	3	1	4	5	4	4	3	4	4	3	4
2	3	2	4	4	3	4	4	2	4	3	4	5	4	2	3	2	2	2	2
4	4	4	4	4	4	5	5	3	4	3	5	5	5	4	3	4	3	4	4
4	4	2	4	3	5	4	4	4	4	2	4	5	5	3	3	4	3	3	3
4	5	4	4	4	3	4	5	4	4	1	5	5	3	3	3	3	4	5	5
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
4	3	4	2	4	2	4	3	2	4	3	4	5	4	4	1	2	3	4	4
3	4	3	3	4	2	3	3	2	2	4	3	2	2	3	3	2	3	3	4
4	2	4	4	2	4	3	3	2	3	4	3	2	2	3	3	2	4	3	3
3	3	2	2	1	1	2	1	4	2	3	2	1	2	3	4	2	3	2	4
3	4	3	4	4	4	3	4	2	3	1	4	2	3	3	2	3	3	4	3
3	3	2	4	4	3	4	1	3	3	1	3	5	2	3	1	4	4	4	4
4	4	5	3	3	3	4	4	3	4	3	5	4	4	4	2	4	4	3	4
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
5	2	4	2	5	1	4	3	2	2	4	5	4	3	3	2	2	4	4	4
2	2	1	1	2	2	3	1	2	2	4	1	2	2	2	3	2	1	2	2
4	2	3	4	3	2	3	4	2	4	4	3	5	2	3	2	4	4	3	4
5	5	5	5	3	5	5	5	5	5	1	5	5	2	5	4	4	5	4	4
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
4	3	4	2	4	3	2	4	4	3	2	5	5	2	4	1	3	5	4	2
4	4	4	3	4	4	4	2	3	4	3	3	4	4	3	3	2	3	4	2
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
4	4	3	4	4	4	4	3	3	4	3	4	4	5	2	1	2	3	4	1
4	4	4	4	3	4	4	4	1	5	2	4	4	4	4	2	3	4	4	4
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
5	5	5	4	5	4	5	5	5	5	1	5	5	5	4	4	5	5	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=308134&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=308134&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308134&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
A1[t] = + 0.427877 + 0.183192A2[t] + 0.241803A3[t] + 0.039571A4[t] -0.0476868A5[t] -0.0324169A6[t] + 0.138384A7[t] -0.0376854A8[t] + 0.112003A9[t] + 0.0107423A10[t] -0.00937631A11[t] + 0.103279A12[t] -0.0255524A13[t] + 0.00302162A14[t] + 0.0134102A15[t] -0.101529A16[t] + 0.00296377A17[t] -0.0152934A18[t] + 0.0849114A19[t] + 0.205745A20[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
A1[t] =  +  0.427877 +  0.183192A2[t] +  0.241803A3[t] +  0.039571A4[t] -0.0476868A5[t] -0.0324169A6[t] +  0.138384A7[t] -0.0376854A8[t] +  0.112003A9[t] +  0.0107423A10[t] -0.00937631A11[t] +  0.103279A12[t] -0.0255524A13[t] +  0.00302162A14[t] +  0.0134102A15[t] -0.101529A16[t] +  0.00296377A17[t] -0.0152934A18[t] +  0.0849114A19[t] +  0.205745A20[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308134&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]A1[t] =  +  0.427877 +  0.183192A2[t] +  0.241803A3[t] +  0.039571A4[t] -0.0476868A5[t] -0.0324169A6[t] +  0.138384A7[t] -0.0376854A8[t] +  0.112003A9[t] +  0.0107423A10[t] -0.00937631A11[t] +  0.103279A12[t] -0.0255524A13[t] +  0.00302162A14[t] +  0.0134102A15[t] -0.101529A16[t] +  0.00296377A17[t] -0.0152934A18[t] +  0.0849114A19[t] +  0.205745A20[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308134&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308134&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
A1[t] = + 0.427877 + 0.183192A2[t] + 0.241803A3[t] + 0.039571A4[t] -0.0476868A5[t] -0.0324169A6[t] + 0.138384A7[t] -0.0376854A8[t] + 0.112003A9[t] + 0.0107423A10[t] -0.00937631A11[t] + 0.103279A12[t] -0.0255524A13[t] + 0.00302162A14[t] + 0.0134102A15[t] -0.101529A16[t] + 0.00296377A17[t] -0.0152934A18[t] + 0.0849114A19[t] + 0.205745A20[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)	+0.4279	0.5553	+7.7060e-01	0.4419	0.2209
A2	+0.1832	0.06841	+2.6780e+00	0.008018	0.004009
A3	+0.2418	0.06682	+3.6190e+00	0.0003744	0.0001872
A4	+0.03957	0.06633	+5.9650e-01	0.5515	0.2757
A5	-0.04769	0.07693	-6.1990e-01	0.536	0.268
A6	-0.03242	0.07153	-4.5320e-01	0.6509	0.3255
A7	+0.1384	0.07289	+1.8990e+00	0.05906	0.02953
A8	-0.03769	0.08721	-4.3210e-01	0.6661	0.3331
A9	+0.112	0.06419	+1.7450e+00	0.08254	0.04127
A10	+0.01074	0.07977	+1.3470e-01	0.893	0.4465
A11	-0.009376	0.05201	-1.8030e-01	0.8571	0.4286
A12	+0.1033	0.07951	+1.2990e+00	0.1955	0.09773
A13	-0.02555	0.05535	-4.6160e-01	0.6448	0.3224
A14	+0.003022	0.06679	+4.5240e-02	0.964	0.482
A15	+0.01341	0.07627	+1.7580e-01	0.8606	0.4303
A16	-0.1015	0.05997	-1.6930e+00	0.09202	0.04601
A17	+0.002964	0.06791	+4.3640e-02	0.9652	0.4826
A18	-0.01529	0.06621	-2.3100e-01	0.8176	0.4088
A19	+0.08491	0.0715	+1.1880e+00	0.2364	0.1182
A20	+0.2057	0.05828	+3.5300e+00	0.000514	0.000257

\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) & +0.4279 &  0.5553 & +7.7060e-01 &  0.4419 &  0.2209 \tabularnewline
A2 & +0.1832 &  0.06841 & +2.6780e+00 &  0.008018 &  0.004009 \tabularnewline
A3 & +0.2418 &  0.06682 & +3.6190e+00 &  0.0003744 &  0.0001872 \tabularnewline
A4 & +0.03957 &  0.06633 & +5.9650e-01 &  0.5515 &  0.2757 \tabularnewline
A5 & -0.04769 &  0.07693 & -6.1990e-01 &  0.536 &  0.268 \tabularnewline
A6 & -0.03242 &  0.07153 & -4.5320e-01 &  0.6509 &  0.3255 \tabularnewline
A7 & +0.1384 &  0.07289 & +1.8990e+00 &  0.05906 &  0.02953 \tabularnewline
A8 & -0.03769 &  0.08721 & -4.3210e-01 &  0.6661 &  0.3331 \tabularnewline
A9 & +0.112 &  0.06419 & +1.7450e+00 &  0.08254 &  0.04127 \tabularnewline
A10 & +0.01074 &  0.07977 & +1.3470e-01 &  0.893 &  0.4465 \tabularnewline
A11 & -0.009376 &  0.05201 & -1.8030e-01 &  0.8571 &  0.4286 \tabularnewline
A12 & +0.1033 &  0.07951 & +1.2990e+00 &  0.1955 &  0.09773 \tabularnewline
A13 & -0.02555 &  0.05535 & -4.6160e-01 &  0.6448 &  0.3224 \tabularnewline
A14 & +0.003022 &  0.06679 & +4.5240e-02 &  0.964 &  0.482 \tabularnewline
A15 & +0.01341 &  0.07627 & +1.7580e-01 &  0.8606 &  0.4303 \tabularnewline
A16 & -0.1015 &  0.05997 & -1.6930e+00 &  0.09202 &  0.04601 \tabularnewline
A17 & +0.002964 &  0.06791 & +4.3640e-02 &  0.9652 &  0.4826 \tabularnewline
A18 & -0.01529 &  0.06621 & -2.3100e-01 &  0.8176 &  0.4088 \tabularnewline
A19 & +0.08491 &  0.0715 & +1.1880e+00 &  0.2364 &  0.1182 \tabularnewline
A20 & +0.2057 &  0.05828 & +3.5300e+00 &  0.000514 &  0.000257 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308134&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]+0.4279[/C][C] 0.5553[/C][C]+7.7060e-01[/C][C] 0.4419[/C][C] 0.2209[/C][/ROW]
[ROW][C]A2[/C][C]+0.1832[/C][C] 0.06841[/C][C]+2.6780e+00[/C][C] 0.008018[/C][C] 0.004009[/C][/ROW]
[ROW][C]A3[/C][C]+0.2418[/C][C] 0.06682[/C][C]+3.6190e+00[/C][C] 0.0003744[/C][C] 0.0001872[/C][/ROW]
[ROW][C]A4[/C][C]+0.03957[/C][C] 0.06633[/C][C]+5.9650e-01[/C][C] 0.5515[/C][C] 0.2757[/C][/ROW]
[ROW][C]A5[/C][C]-0.04769[/C][C] 0.07693[/C][C]-6.1990e-01[/C][C] 0.536[/C][C] 0.268[/C][/ROW]
[ROW][C]A6[/C][C]-0.03242[/C][C] 0.07153[/C][C]-4.5320e-01[/C][C] 0.6509[/C][C] 0.3255[/C][/ROW]
[ROW][C]A7[/C][C]+0.1384[/C][C] 0.07289[/C][C]+1.8990e+00[/C][C] 0.05906[/C][C] 0.02953[/C][/ROW]
[ROW][C]A8[/C][C]-0.03769[/C][C] 0.08721[/C][C]-4.3210e-01[/C][C] 0.6661[/C][C] 0.3331[/C][/ROW]
[ROW][C]A9[/C][C]+0.112[/C][C] 0.06419[/C][C]+1.7450e+00[/C][C] 0.08254[/C][C] 0.04127[/C][/ROW]
[ROW][C]A10[/C][C]+0.01074[/C][C] 0.07977[/C][C]+1.3470e-01[/C][C] 0.893[/C][C] 0.4465[/C][/ROW]
[ROW][C]A11[/C][C]-0.009376[/C][C] 0.05201[/C][C]-1.8030e-01[/C][C] 0.8571[/C][C] 0.4286[/C][/ROW]
[ROW][C]A12[/C][C]+0.1033[/C][C] 0.07951[/C][C]+1.2990e+00[/C][C] 0.1955[/C][C] 0.09773[/C][/ROW]
[ROW][C]A13[/C][C]-0.02555[/C][C] 0.05535[/C][C]-4.6160e-01[/C][C] 0.6448[/C][C] 0.3224[/C][/ROW]
[ROW][C]A14[/C][C]+0.003022[/C][C] 0.06679[/C][C]+4.5240e-02[/C][C] 0.964[/C][C] 0.482[/C][/ROW]
[ROW][C]A15[/C][C]+0.01341[/C][C] 0.07627[/C][C]+1.7580e-01[/C][C] 0.8606[/C][C] 0.4303[/C][/ROW]
[ROW][C]A16[/C][C]-0.1015[/C][C] 0.05997[/C][C]-1.6930e+00[/C][C] 0.09202[/C][C] 0.04601[/C][/ROW]
[ROW][C]A17[/C][C]+0.002964[/C][C] 0.06791[/C][C]+4.3640e-02[/C][C] 0.9652[/C][C] 0.4826[/C][/ROW]
[ROW][C]A18[/C][C]-0.01529[/C][C] 0.06621[/C][C]-2.3100e-01[/C][C] 0.8176[/C][C] 0.4088[/C][/ROW]
[ROW][C]A19[/C][C]+0.08491[/C][C] 0.0715[/C][C]+1.1880e+00[/C][C] 0.2364[/C][C] 0.1182[/C][/ROW]
[ROW][C]A20[/C][C]+0.2057[/C][C] 0.05828[/C][C]+3.5300e+00[/C][C] 0.000514[/C][C] 0.000257[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308134&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308134&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)	+0.4279	0.5553	+7.7060e-01	0.4419	0.2209
A2	+0.1832	0.06841	+2.6780e+00	0.008018	0.004009
A3	+0.2418	0.06682	+3.6190e+00	0.0003744	0.0001872
A4	+0.03957	0.06633	+5.9650e-01	0.5515	0.2757
A5	-0.04769	0.07693	-6.1990e-01	0.536	0.268
A6	-0.03242	0.07153	-4.5320e-01	0.6509	0.3255
A7	+0.1384	0.07289	+1.8990e+00	0.05906	0.02953
A8	-0.03769	0.08721	-4.3210e-01	0.6661	0.3331
A9	+0.112	0.06419	+1.7450e+00	0.08254	0.04127
A10	+0.01074	0.07977	+1.3470e-01	0.893	0.4465
A11	-0.009376	0.05201	-1.8030e-01	0.8571	0.4286
A12	+0.1033	0.07951	+1.2990e+00	0.1955	0.09773
A13	-0.02555	0.05535	-4.6160e-01	0.6448	0.3224
A14	+0.003022	0.06679	+4.5240e-02	0.964	0.482
A15	+0.01341	0.07627	+1.7580e-01	0.8606	0.4303
A16	-0.1015	0.05997	-1.6930e+00	0.09202	0.04601
A17	+0.002964	0.06791	+4.3640e-02	0.9652	0.4826
A18	-0.01529	0.06621	-2.3100e-01	0.8176	0.4088
A19	+0.08491	0.0715	+1.1880e+00	0.2364	0.1182
A20	+0.2057	0.05828	+3.5300e+00	0.000514	0.000257

Multiple Linear Regression - Regression Statistics
Multiple R	0.5301
R-squared	0.281
Adjusted R-squared	0.2134
F-TEST (value)	4.156
F-TEST (DF numerator)	19
F-TEST (DF denominator)	202
p-value	1.178e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.807
Sum Squared Residuals	131.5

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5301 \tabularnewline
R-squared &  0.281 \tabularnewline
Adjusted R-squared &  0.2134 \tabularnewline
F-TEST (value) &  4.156 \tabularnewline
F-TEST (DF numerator) & 19 \tabularnewline
F-TEST (DF denominator) & 202 \tabularnewline
p-value &  1.178e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.807 \tabularnewline
Sum Squared Residuals &  131.5 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308134&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5301[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.281[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.2134[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 4.156[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]19[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]202[/C][/ROW]
[ROW][C]p-value[/C][C] 1.178e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 0.807[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 131.5[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308134&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308134&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	0.5301
R-squared	0.281
Adjusted R-squared	0.2134
F-TEST (value)	4.156
F-TEST (DF numerator)	19
F-TEST (DF denominator)	202
p-value	1.178e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.807
Sum Squared Residuals	131.5

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=308134&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=308134&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308134&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 = 0.041658, df1 = 2, df2 = 200, p-value = 0.9592
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.6078, df1 = 38, df2 = 164, p-value = 0.02263
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.47674, df1 = 2, df2 = 200, p-value = 0.6215

\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 = 0.041658, df1 = 2, df2 = 200, p-value = 0.9592
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.6078, df1 = 38, df2 = 164, p-value = 0.02263
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.47674, df1 = 2, df2 = 200, p-value = 0.6215
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308134&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 = 0.041658, df1 = 2, df2 = 200, p-value = 0.9592
[/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 = 1.6078, df1 = 38, df2 = 164, p-value = 0.02263
[/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 = 0.47674, df1 = 2, df2 = 200, p-value = 0.6215
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308134&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308134&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 = 0.041658, df1 = 2, df2 = 200, p-value = 0.9592
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.6078, df1 = 38, df2 = 164, p-value = 0.02263
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.47674, df1 = 2, df2 = 200, p-value = 0.6215

Variance Inflation Factors (Multicollinearity)

> vif
      A2       A3       A4       A5       A6       A7       A8       A9 
1.183257 1.371758 1.198323 1.475557 1.483932 1.426074 1.902678 1.211195 
     A10      A11      A12      A13      A14      A15      A16      A17 
1.610877 1.254420 1.477709 1.148423 1.366511 1.336597 1.201456 1.214596 
     A18      A19      A20 
1.301305 1.251613 1.270033

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
      A2       A3       A4       A5       A6       A7       A8       A9 
1.183257 1.371758 1.198323 1.475557 1.483932 1.426074 1.902678 1.211195 
     A10      A11      A12      A13      A14      A15      A16      A17 
1.610877 1.254420 1.477709 1.148423 1.366511 1.336597 1.201456 1.214596 
     A18      A19      A20 
1.301305 1.251613 1.270033 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308134&T=6

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
      A2       A3       A4       A5       A6       A7       A8       A9 
1.183257 1.371758 1.198323 1.475557 1.483932 1.426074 1.902678 1.211195 
     A10      A11      A12      A13      A14      A15      A16      A17 
1.610877 1.254420 1.477709 1.148423 1.366511 1.336597 1.201456 1.214596 
     A18      A19      A20 
1.301305 1.251613 1.270033 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308134&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308134&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
      A2       A3       A4       A5       A6       A7       A8       A9 
1.183257 1.371758 1.198323 1.475557 1.483932 1.426074 1.902678 1.211195 
     A10      A11      A12      A13      A14      A15      A16      A17 
1.610877 1.254420 1.477709 1.148423 1.366511 1.336597 1.201456 1.214596 
     A18      A19      A20 
1.301305 1.251613 1.270033

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 500 ; par2 = 12 ; par3 = 5 ; par4 = P1 P5 Q1 Q3 P95 P99 ;

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