# AP Statistics Curriculum 2007 EDA Plots

### From Socr

(Difference between revisions)

(3 intermediate revisions not shown) | |||

Line 1: | Line 1: | ||

==[[AP_Statistics_Curriculum_2007 | General Advance-Placement (AP) Statistics Curriculum]] - Graphs & Exploratory Data Analysis== | ==[[AP_Statistics_Curriculum_2007 | General Advance-Placement (AP) Statistics Curriculum]] - Graphs & Exploratory Data Analysis== | ||

- | === | + | === Exploratory Data Analysis (EDA)=== |

- | + | Modern statistics regards the graphical visualization and interrogation of data as a critical component of any reliable method for statistical modeling, analysis and interpretation of data. Formally, there are two types of data analysis that should be employed in concert on the same set of data to make a valid and robust inference. The objectives of EDA are to: | |

- | + | * Suggest hypotheses about the causes of observed phenomena | |

+ | * Assess (parametric) assumptions on which statistical inference will be based | ||

+ | * Support the selection of appropriate statistical tools and techniques | ||

+ | * Provide a basis for further data collection through surveys or experiments | ||

===Approach=== | ===Approach=== | ||

- | + | Many EDA techniques have been proposed, validated and adopted for various statistical methodologies. For some of these we have discussed in the [[AP_Statistics_Curriculum_2007#Pictures_of_Data | Data visualization section]]. Other frequently used EDA charts include: | |

- | + | * [[SOCR_EduMaterials_Activities_BoxPlot | Box-and-Whisker plot]] | |

- | * | + | * [[SOCR_EduMaterials_Activities_Histogram_Graphs | Histogram]] |

- | + | * [[SOCR_EduMaterials_Activities_DotChart | Dot plot]] | |

- | + | * [[SOCR_EduMaterials_Activities_ScatterChart | Scatter plot]] | |

- | + | * [http://en.wikipedia.org/wiki/Stem_and_leaf Stem-and-leaf plot] | |

- | + | * [[SOCR_EduMaterials_Activities_IndexChart | Index plot]] | |

- | * | + | * [[SOCR_EduMaterials_Activities_QQChart | QQ Normal Plot]] |

- | + | ||

- | + | ||

- | * | + | |

===Examples=== | ===Examples=== | ||

- | + | [[NISER_081107_ID#Statistics | This activity]] provides hands-on demonstration of EDA on a large data set of [[NISER_081107_ID_Data | Mercury in Bass]]. | |

- | + | ||

- | + | ||

- | + | ||

- | + | ||

- | + | ||

- | + | ===[[EBook_Problems_EDA_Plots | Problems]]=== | |

<hr> | <hr> |

## Current revision as of 16:44, 28 June 2010

## Contents |

## General Advance-Placement (AP) Statistics Curriculum - Graphs & Exploratory Data Analysis

### Exploratory Data Analysis (EDA)

Modern statistics regards the graphical visualization and interrogation of data as a critical component of any reliable method for statistical modeling, analysis and interpretation of data. Formally, there are two types of data analysis that should be employed in concert on the same set of data to make a valid and robust inference. The objectives of EDA are to:

- Suggest hypotheses about the causes of observed phenomena
- Assess (parametric) assumptions on which statistical inference will be based
- Support the selection of appropriate statistical tools and techniques
- Provide a basis for further data collection through surveys or experiments

### Approach

Many EDA techniques have been proposed, validated and adopted for various statistical methodologies. For some of these we have discussed in the Data visualization section. Other frequently used EDA charts include:

### Examples

This activity provides hands-on demonstration of EDA on a large data set of Mercury in Bass.

### Problems

### References

- TBD

- SOCR Home page: http://www.socr.ucla.edu

Translate this page: