SOCR EduMaterials Activities General CI Experiment

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(New page: == SOCR Experiments Activities - General Confidence Interval Activity == == Summary== This activity demonstrates the usage and functionality of [http://s...)
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== [[SOCR_EduMaterials_Activities| SOCR Experiments Activities]] - General Confidence Interval Activity ==
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== [[SOCR_EduMaterials_ExperimentsActivities | SOCR Experiments Activities]] - General Confidence Interval Activity ==
== Summary==
== Summary==
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This activity demonstrates the usage and functionality of [http://socr.ucla.edu/SOCR_MotionCharts/ SOCR MotionCharts] using the [[SOCR_Data_Dinov_010309_HousingPriceIndex | SOCR Housing Price Index dataset]].  
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There are two types of parameter estimates – ''point-based'' and ''interval-based'' estimates. Point-estimates refer to unique quantitative estimates of various parameters. Interval-estimates represent ranges of plausible values for the parameters of interest. There are different algorithmic approaches, prior assumptions and principals for computing data-driven parameter estimates. Both point and interval estimates depend on the distribution of the process of interest, the available computational resources and other criteria that may be desirable (Stewarty 1999) – e.g., [http://en.wikipedia.org/wiki/Bias_of_an_estimator biasness] and [http://en.wikipedia.org/wiki/Robust_statistics robustness] of the estimates. Accurate, robust and efficient parameter estimation is critical in making inference about observable experiments, summarizing process characteristics and prediction of experimental behaviors.
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This activity demonstrates the usage and functionality of [http://socr.ucla.edu/htmls/exp/Confidence_Interval_Experiment_General.html SOCR General Confidence Interval Applet]. This applet is complementary to the [http://socr.ucla.edu/htmls/exp/Confidence_Interval_Experiment.html SOCR Simple Confidence Interval Applet] and its corresponding [[SOCR_EduMaterials_Activities_CoinfidenceIntervalExperiment | activity]].
==Goals==
==Goals==
The aims of this activity is to:
The aims of this activity is to:
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* demonstrate SOCR MotionCharts data import, manipulations and graphical interpretation
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* TBD
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* interactively explore the graphical visualization of real-life multidimensional datasets
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* TBD
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* complex data navigation from different directions (using data mappings).
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* TBD.
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==Background ==
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==Motivational example==
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The amount, complexity and provenance of data has dramatically increased in the last few years. Visualization of observed and simulated data is a critical component of any social, environmental, biomedical or scientific quest. Dynamic, exploratory and interactive visualization of multivariate data, without preprocessing by dimensionality reduction, remains an insurmountable challenge. [http://socr.ucla.edu/SOCR_MotionCharts/ SOCR MotionCharts] provide a new paradigm for discovery-based exploratory analysis of multivariate data. This interactive data visualization tool enables the visualization of high-dimensional longitudinal data. SOCR Motion Charts allows mapping of ordinal, nominal and quantitative variables onto time, axes, size, colors, glyphs and appearance characteristics, which facilitates the interactive display of multidimensional data. SOCR Motion Charts can be used as instructional tool for rendering and interrogating high-dimensional data in the classroom, as well as a research tool for exploratory data analysis.
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[[Image:SOCR_Activities_General_CI_Activity_070709_Fig1.png|150px|thumbnail|right| [http://www.stat.ucla.edu/%7Edinov/courses_students.dir/04/Spring/Stat233.dir/HWs.dir/AD_NeuroPsychImagingData1.html Alzheimer's Disease Dataset]]]
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A 2005 study proposing a new computational brain atlas for Alzheimer’s disease (Mega et al., 2005) investigated the mean volumetric characteristics and the spectra of shapes and sizes of different cortical and subcortical brain regions for Alzheimer’s patients, individuals with minor cognitive impairment and asymptomatic subjects. This study estimated a number of centrality and variability parameters for these thee populations. Based on these point- and interval-estimates, the study analyzed a number of digital scans to derive criteria for imaging-based classification of subjects based on the intensities of their 3D brain scans. Their results enabled a number of subsequent inference studies that quantified the effects of subject demographics (e.g., education level, familial history, APOE allele, etc.), stage of the disease and the efficacy of new drug treatments targeting Alzheimer’s disease. The Figure to the right illustrates the ''shape, center'' and ''distribution parameters'' for the 3D geometric structure of the right hippocampus in the Alzheimer’s disease brain atlas. New imaging data can then be coregistered and compared relative to the amount of anatomical variability encoded in this atlas. This enables automated, efficient and quantitative inference on large number of brain volumes. Examples of point and interval estimates computed in this atlas framework include the mean-intensity and mean shape location, and the standard deviation of intensities and the mean deviation of shape, respectively.
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== Description ==
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In addition to this activity, open 2 more browser tabs - one pointing to the [http://socr.ucla.edu/SOCR_MotionCharts/ SOCR MotionCharts applet] and the other displaying the [[SOCR_Data_Dinov_010309_HousingPriceIndex | SOCR Housing Price Index dataset]]. The image below shows the arrangement of these 3 browser tabs.  
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<center>[[Image:SOCR_Activities_MotionCharts_HPI_070109_Fig1.png|250px]] [[Image:SOCR_Activities_MotionCharts_HPI_070109_Fig2.png|250px]] [[Image:SOCR_Activities_MotionCharts_HPI_070109_Fig3.png|250px]]
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</center>
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==Activity==
==Activity==
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The [[SOCR_Data_Dinov_010309_HousingPriceIndex | house price index]] data was provided by the Office of Federal Housing Enterprise Oversight. The data represents the average housing price for all states between the years 2000 and 2006. The data also includes the average unemployment rate, population (in thousands), the percent subprime loans, and the region by state.
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TBD!
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* Using the mouse, copy the [[SOCR_Data_Dinov_010309_HousingPriceIndex |data from the SOCR data web page]], click on the first cell (top-left) in the DATA tab of the [http://socr.ucla.edu/SOCR_MotionCharts/ SOCR Motion Charts applet], and paste the data in the spreadsheet.
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In addition to this activity, open 2 more browser tabs - one pointing to the [http://socr.ucla.edu/htmls/exp/Confidence_Interval_Experiment_General.html SOCR General CI applet] and the other displaying the [[SOCR_Data | SOCR (WHICH ONE?) dataset]]. The images below show the arrangement of these 3 browser tabs.  
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<center>[[Image:SOCR_Activities_MotionCharts_HPI_070109_Fig4.png|400px]]</center>
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<center>[[Image:SOCR_Activities_MotionCharts_HPI_070109_Fig1.png|250px]] [[Image:SOCR_Activities_MotionCharts_HPI_070109_Fig2.png|250px]] [[Image:SOCR_Activities_MotionCharts_HPI_070109_Fig3.png|250px]]
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* Next, you need to map the column-variables to different properties it the SOCR MotionChart. For example, you can use the following mapping:
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<center>
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[[Image:SOCR_Activities_MotionCharts_HPI_070109_Fig5.png|150px|thumbnail|right| SOCR MotionChart Data Mapping]]
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{| class="wikitable"
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|-
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! || colspan=6 | Variables
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|-
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! [http://socr.ucla.edu/SOCR_MotionCharts/ SOCR MotionChart Property] || Key || X-Axis || Y-Axis || Size || Color || Category
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|-
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! [[SOCR_Data_Dinov_010309_HousingPriceIndex | Data Column Name]] || Year || HPI || UR || Pop || Region || State
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|}
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</center>
</center>
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The figures below represent snapshots of the generated dynamic SOCR motion chart. In the real applet, you can ''play'' (animate) or ''scroll'' (1-year steps) through the years (2000, ..., 2006). Notice the position change between different snapshots of the time slider on the bottom of these figures. Also, mouse-over a blob triggers a dynamic graphical pop-up providing additional information about the data for the specified blob in the chart.
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* List the steps ....
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<center>[[Image:SOCR_Activities_MotionCharts_HPI_070109_Fig6.png|400px]]
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[[Image:SOCR_Activities_MotionCharts_HPI_070109_Fig7.png|400px]]
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[[Image:SOCR_Activities_MotionCharts_HPI_070109_Fig8.png|400px]]
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[[Image:SOCR_Activities_MotionCharts_HPI_070109_Fig9.png|400px]]
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</center>
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You can also change what variables (data columns) are mapped to the following SOCR MotionCharts properties:
 
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* ''Key, X-Axis, Y-Axis, Size, Color'' and ''Category''. Changing the variable-property mapping allows you to explore the data from a different perspective.
 
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== Data type and format ==
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== Applet Navigation ==
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SOCR Motion Charts currently accepts three types of data: numbers, dates/time, and strings. With these data types, the SOCR MotionCharts applet is able to handle the majority of data types. Internally, the applet uses the ''natural (lexicological) ordering'' of these data as defined by Java primitive data types. While many types of data can be interpreted as strings, in some cases it may not be appropriate to use string-data lexicological ordering. When designing SOCR MotionCharts, we took this into consideration and designed the applet so that it can easily be extended to provide a greater variety of interpreted types. Thus, a developer may easily extend the applet to provide another data type interpretation for specific types of data.
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TBD
== Applications ==
== Applications ==
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The SOCR MotionCharts can be used in a variety of applications to visualize dynamic relationships in multidimensional data in up to 5 dimensions, plus a 6 temporal component. The applet's design and implementation allow for extensions enabling and supporting higher dimensions plug-ins. The overall purpose of SOCR MotionCharts is to provide users with a way to visualize the relationships between multiple variables over a period of time in a simple, intuitive and animated fashion.
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TBDn.
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==References==
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* Mega, M., Dinov, I., Thompson, P., Manese, M., Lindshield, C., Moussai, J., Tran, N., Olsen, K., Felix, J., Zoumalan, C., Woods, R., Toga, A., and Mazziotta, J. (2005). ''Automated brain tissue assessment in the elderly and demented population: Construction and validation of a sub-volume probabilistic brain atlas''. NeuroImage, 26(4), 1009-1018.
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* Stewarty, C. (1999). ''Robust Parameter Estimation in Computer Vision''. SIAM Review, 41(3), 513–537.
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* Wolfram, S. (2002). ''A New Kind of Science'', Wolfram Media Inc.
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Revision as of 18:16, 7 July 2009

Contents

SOCR Experiments Activities - General Confidence Interval Activity

Summary

There are two types of parameter estimates – point-based and interval-based estimates. Point-estimates refer to unique quantitative estimates of various parameters. Interval-estimates represent ranges of plausible values for the parameters of interest. There are different algorithmic approaches, prior assumptions and principals for computing data-driven parameter estimates. Both point and interval estimates depend on the distribution of the process of interest, the available computational resources and other criteria that may be desirable (Stewarty 1999) – e.g., biasness and robustness of the estimates. Accurate, robust and efficient parameter estimation is critical in making inference about observable experiments, summarizing process characteristics and prediction of experimental behaviors.

This activity demonstrates the usage and functionality of SOCR General Confidence Interval Applet. This applet is complementary to the SOCR Simple Confidence Interval Applet and its corresponding activity.

Goals

The aims of this activity is to:

  • TBD
  • TBD
  • TBD.

Motivational example

A 2005 study proposing a new computational brain atlas for Alzheimer’s disease (Mega et al., 2005) investigated the mean volumetric characteristics and the spectra of shapes and sizes of different cortical and subcortical brain regions for Alzheimer’s patients, individuals with minor cognitive impairment and asymptomatic subjects. This study estimated a number of centrality and variability parameters for these thee populations. Based on these point- and interval-estimates, the study analyzed a number of digital scans to derive criteria for imaging-based classification of subjects based on the intensities of their 3D brain scans. Their results enabled a number of subsequent inference studies that quantified the effects of subject demographics (e.g., education level, familial history, APOE allele, etc.), stage of the disease and the efficacy of new drug treatments targeting Alzheimer’s disease. The Figure to the right illustrates the shape, center and distribution parameters for the 3D geometric structure of the right hippocampus in the Alzheimer’s disease brain atlas. New imaging data can then be coregistered and compared relative to the amount of anatomical variability encoded in this atlas. This enables automated, efficient and quantitative inference on large number of brain volumes. Examples of point and interval estimates computed in this atlas framework include the mean-intensity and mean shape location, and the standard deviation of intensities and the mean deviation of shape, respectively.

Activity

TBD!

In addition to this activity, open 2 more browser tabs - one pointing to the SOCR General CI applet and the other displaying the SOCR (WHICH ONE?) dataset. The images below show the arrangement of these 3 browser tabs.

  • List the steps ....


Applet Navigation

TBD

Applications

TBDn.

References

  • Mega, M., Dinov, I., Thompson, P., Manese, M., Lindshield, C., Moussai, J., Tran, N., Olsen, K., Felix, J., Zoumalan, C., Woods, R., Toga, A., and Mazziotta, J. (2005). Automated brain tissue assessment in the elderly and demented population: Construction and validation of a sub-volume probabilistic brain atlas. NeuroImage, 26(4), 1009-1018.
  • Stewarty, C. (1999). Robust Parameter Estimation in Computer Vision. SIAM Review, 41(3), 513–537.
  • Wolfram, S. (2002). A New Kind of Science, Wolfram Media Inc.


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