サインイン

An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the vertical distance from the line to the point.

Outliers need to be examined closely. Sometimes, for some reason or another, they should not be included in the data analysis. An outlier may be a result of erroneous data. Other times, an outlier may hold valuable information about the population under study and should remain included in the data. The key is carefully examining what causes a data point to be an outlier.

Besides outliers, a sample may contain one or a few points that are called influential points. Influential points are observed data points that are far from the other observed data points in the horizontal direction. These points may have a significant effect on the slope of the regression line. To identify an influential point, you can remove it from the data set and see if the slope of the regression line is changed significantly.

Computers and many calculators can be used to identify outliers from the data. Computer output for regression analysis will often identify both outliers and influential points so that you can examine them.

This text is adapted from Openstax, Introductory Statistics, Section 12.6 Outliers

タグ

OutliersInfluential PointsExtreme ValuesData AnalysisLeast Squares LineRegression LineData PointsVertical DistanceHorizontal DirectionResidualsData ExaminationStatistical AnalysisOpenstax

章から 11:

article

Now Playing

11.5 : Outliers and Influential Points

相関と回帰

3.8K 閲覧数

article

11.1 : 相関

相関と回帰

11.2K 閲覧数

article

11.2 : 相関係数

相関と回帰

5.8K 閲覧数

article

11.3 : 線形相関係数の計算と解釈

相関と回帰

5.6K 閲覧数

article

11.4 : 回帰分析

相関と回帰

5.3K 閲覧数

article

11.6 : 残差と最小二乗法のプロパティ

相関と回帰

7.0K 閲覧数

article

11.7 : 残差プロット

相関と回帰

4.3K 閲覧数

article

11.8 : バリエーション

相関と回帰

6.4K 閲覧数

article

11.9 : 予測区間

相関と回帰

2.2K 閲覧数

article

11.10 : 重回帰

相関と回帰

2.8K 閲覧数

JoVE Logo

個人情報保護方針

利用規約

一般データ保護規則

研究

教育

JoVEについて

Copyright © 2023 MyJoVE Corporation. All rights reserved