AP Statistics Curriculum 2007 EDA Plots

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=== Exploratory Data Analysis (EDA)===
=== Exploratory Data Analysis (EDA)===
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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:
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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
* Suggest hypotheses about the causes of observed phenomena
* Assess (parametric) assumptions on which statistical inference will be based
* Assess (parametric) assumptions on which statistical inference will be based
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===Approach===
===Approach===
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Many EDA techniques have been proposed, validated and adopted for various statistical methodologies. Some of these we already discussed in the [[AP_Statistics_Curriculum_2007#Pictures_of_Data | Data visualization section]]. Other frequently used EDA charts include:
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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_BoxPlot | Box-and-Whisker plot]]
* [[SOCR_EduMaterials_Activities_Histogram_Graphs | Histogram]]
* [[SOCR_EduMaterials_Activities_Histogram_Graphs | Histogram]]

Current revision as of 16:42, 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



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