#+TITLE: Rapport package team
#+AUTHOR: Outlier tests
#+DATE: 2011-04-26 20:25 CET
** Description
This template will check if provided variable has any outliers.
*** Introduction
An outlying observation, or outlier, is one that appears to deviate
markedly from other members of the sample in which it occurs. There are
several ways to detect the outliers of our data. However, we cannot say
one of them is the perfect method for that, thus it could be useful to
take different methods into consideration. We present here four of them,
one by a chart (a Box Plot based on IQR) and three by statistical
descriptions (Lund Test, Grubb's test, Dixon's test).
**** References
- Grubbs, F. E.: 1969, Procedures for detecting outlying observations
in samples. Technometrics 11, pp. 1-21.
*** Charts
Among the graphical displays the Box plots are quite widespread, because
of their several advantages. For example, one can easily get
approximately punctual first impression from the data and one can
visually see the positions of the (possible) outliers, with the help of
them.
The Box Plot we used here is based on IQR (Interquartile Range), which
is the difference between the higher and the lower quartiles. On the
chart the blue box shows the "middle-half" of the data, the so-called
whiskers shows the border where from the possible values can be called
outliers. The lower whisker is placed 1.5 times below the first
quartile, similarly the higher whisker 1.5 times above the third
quartile.
[[plots/OutlierTest-1-hires.png][[[plots/OutlierTest-1.png]]]]
**** References
- Chambers, John, William Cleveland, Beat Kleiner, and Paul Tukey,
(1983), Graphical Methods for Data Analysis, Wadsworth.
- Upton, Graham; Cook, Ian (1996). Understanding Statistics. Oxford
University Press. p. 55.
*** Lund test
It seems that /4/ extreme values can be found in "Internet usage for
educational purposes (hours per day)". These are: /10/, /0.5/, /1.5/ and
/0.5/.
**** Explanation
The above test for outliers was based on /lm(edu ~ 1)/:
| | Estimate | Std. Error | t value | Pr(>|t|) |
|-----------------+------------+--------------+-----------+--------------|
| *(Intercept)* | 2.048 | 0.07797 | 26.27 | 7.939e-105 |
#+CAPTION: Linear model: edu ~ 1
**** References
- Lund, R. E. 1975, "Tables for An Approximate Test for Outliers in
Linear Models", Technometrics, vol. 17, no. 4, pp. 473-476.
- Prescott, P. 1975, "An Approximate Test for Outliers in Linear
Models", Technometrics, vol. 17, no. 1, pp. 129-132.
*** Grubb's test
Grubbs test for one outlier shows that highest value 12 is an outlier
(p=/0.0001964/).
**** References
- Grubbs, F.E. (1950). Sample Criteria for testing outlying
observations. Ann. Math. Stat. 21, 1, 27-58.
*** Dixon's test
chi-squared test for outlier shows that highest value 12 is an outlier
(p=/7.441e-07/).
**** References
- Dixon, W.J. (1950). Analysis of extreme values. Ann. Math. Stat. 21,
4, 488-506.
** Description
This template will check if provided variable has any outliers.
*** Introduction
An outlying observation, or outlier, is one that appears to deviate
markedly from other members of the sample in which it occurs. There are
several ways to detect the outliers of our data. However, we cannot say
one of them is the perfect method for that, thus it could be useful to
take different methods into consideration. We present here four of them,
one by a chart (a Box Plot based on IQR) and three by statistical
descriptions (Lund Test, Grubb's test, Dixon's test).
**** References
- Grubbs, F. E.: 1969, Procedures for detecting outlying observations
in samples. Technometrics 11, pp. 1-21.
*** Charts
Among the graphical displays the Box plots are quite widespread, because
of their several advantages. For example, one can easily get
approximately punctual first impression from the data and one can
visually see the positions of the (possible) outliers, with the help of
them.
The Box Plot we used here is based on IQR (Interquartile Range), which
is the difference between the higher and the lower quartiles. On the
chart the blue box shows the "middle-half" of the data, the so-called
whiskers shows the border where from the possible values can be called
outliers. The lower whisker is placed 1.5 times below the first
quartile, similarly the higher whisker 1.5 times above the third
quartile.
[[plots/OutlierTest-1-hires.png][[[plots/OutlierTest-1.png]]]]
**** References
- Chambers, John, William Cleveland, Beat Kleiner, and Paul Tukey,
(1983), Graphical Methods for Data Analysis, Wadsworth.
- Upton, Graham; Cook, Ian (1996). Understanding Statistics. Oxford
University Press. p. 55.
*** Lund test
It seems that /4/ extreme values can be found in "Internet usage for
educational purposes (hours per day)". These are: /10/, /0.5/, /1.5/ and
/0.5/.
**** Explanation
The above test for outliers was based on /lm(edu ~ 1)/:
| | Estimate | Std. Error | t value | Pr(>|t|) |
|-----------------+------------+--------------+-----------+--------------|
| *(Intercept)* | 2.048 | 0.07797 | 26.27 | 7.939e-105 |
#+CAPTION: Linear model: edu ~ 1
**** References
- Lund, R. E. 1975, "Tables for An Approximate Test for Outliers in
Linear Models", Technometrics, vol. 17, no. 4, pp. 473-476.
- Prescott, P. 1975, "An Approximate Test for Outliers in Linear
Models", Technometrics, vol. 17, no. 1, pp. 129-132.
*** Grubb's test
Grubbs test for one outlier shows that highest value 12 is an outlier
(p=/0.0001964/).
**** References
- Grubbs, F.E. (1950). Sample Criteria for testing outlying
observations. Ann. Math. Stat. 21, 1, 27-58.
*** Dixon's test
chi-squared test for outlier shows that highest value 12 is an outlier
(p=/7.441e-07/).
**** References
- Dixon, W.J. (1950). Analysis of extreme values. Ann. Math. Stat. 21,
4, 488-506.
** Description
This template will check if provided variable has any outliers.
*** Introduction
An outlying observation, or outlier, is one that appears to deviate
markedly from other members of the sample in which it occurs. There are
several ways to detect the outliers of our data. However, we cannot say
one of them is the perfect method for that, thus it could be useful to
take different methods into consideration. We present here four of them,
one by a chart (a Box Plot based on IQR) and three by statistical
descriptions (Lund Test, Grubb's test, Dixon's test).
**** References
- Grubbs, F. E.: 1969, Procedures for detecting outlying observations
in samples. Technometrics 11, pp. 1-21.
*** Charts
Among the graphical displays the Box plots are quite widespread, because
of their several advantages. For example, one can easily get
approximately punctual first impression from the data and one can
visually see the positions of the (possible) outliers, with the help of
them.
The Box Plot we used here is based on IQR (Interquartile Range), which
is the difference between the higher and the lower quartiles. On the
chart the blue box shows the "middle-half" of the data, the so-called
whiskers shows the border where from the possible values can be called
outliers. The lower whisker is placed 1.5 times below the first
quartile, similarly the higher whisker 1.5 times above the third
quartile.
[[plots/OutlierTest-1-hires.png][[[plots/OutlierTest-1.png]]]]
**** References
- Chambers, John, William Cleveland, Beat Kleiner, and Paul Tukey,
(1983), Graphical Methods for Data Analysis, Wadsworth.
- Upton, Graham; Cook, Ian (1996). Understanding Statistics. Oxford
University Press. p. 55.
*** Lund test
It seems that /4/ extreme values can be found in "Internet usage for
educational purposes (hours per day)". These are: /10/, /0.5/, /1.5/ and
/0.5/.
**** Explanation
The above test for outliers was based on /lm(edu ~ 1)/:
| | Estimate | Std. Error | t value | Pr(>|t|) |
|-----------------+------------+--------------+-----------+--------------|
| *(Intercept)* | 2.048 | 0.07797 | 26.27 | 7.939e-105 |
#+CAPTION: Linear model: edu ~ 1
--------------
This report was generated with [[http://www.r-project.org/][R]] (3.0.1)
and [[https://rapporter.github.io/rapport/][rapport]] (0.51) in /1.082/ sec on
x86\_64-unknown-linux-gnu platform.
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