# AP Statistics Curriculum 2007 EDA Plots

(Difference between revisions)
 Revision as of 05:18, 28 January 2008 (view source)IvoDinov (Talk | contribs)← Older edit Revision as of 05:18, 28 January 2008 (view source)IvoDinov (Talk | contribs) m (→Approach)Newer edit → Line 14: Line 14: * [[SOCR_EduMaterials_Activities_DotChart | Dot plot]] * [[SOCR_EduMaterials_Activities_DotChart | Dot plot]] * [[SOCR_EduMaterials_Activities_ScatterChart | Scatter plot]] * [[SOCR_EduMaterials_Activities_ScatterChart | Scatter plot]] - * [[http://en.wikipedia.org/wiki/Stem_and_leaf | Stem-and-leaf plot]] + * [http://en.wikipedia.org/wiki/Stem_and_leaf Stem-and-leaf plot] * [[SOCR_EduMaterials_Activities_IndexChart | Index plot]] * [[SOCR_EduMaterials_Activities_IndexChart | Index plot]] * [[SOCR_EduMaterials_Activities_QQChart | QQ Normal Plot]] * [[SOCR_EduMaterials_Activities_QQChart | QQ Normal Plot]]

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

### Exploratory Data Analysis (EDA)

Modern statistics regard 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. Some of these we already 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.

• TBD