AP Statistics Curriculum 2007 EDA Var

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===Measures of Variation and Dispersion===
===Measures of Variation and Dispersion===
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Example on how to attach images to Wiki documents in included below (this needs to be replaced by an appropriate figure for this section)!
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There are many measures of (population or sample) variation, e.g., the range, the variance, the standard deviation, mean absolute deviation, etc. These are used to assess the dispersion or spread of the population.
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<center>[[Image:AP_Statistics_Curriculum_2007_IntroVar_Dinov_061407_Fig1.png|500px]]</center>
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===Approach===
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Suppose we are interested in the long-jump performance of some students. We can carry an experiment by randomly selecting 8 male statistics students and ask them to perform the standing long jump.  In reality every student participated, but for the ease of calculations below we will focus on these eight students. The long jumps were as follows:
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Models & strategies for solving the problem, data understanding & inference.  
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* TBD
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{| class="wikitable" style="text-align:center; width:75%" border="1"
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|+Long-Jump (inches) Sample Data
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|-
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| 74 || 78 || 106 || 80 || 68 || 64 || 60 || 76
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|}
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===Model Validation===
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===Range===
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Checking/affirming underlying assumptions.  
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The range is the easiest measure of dispersion to calculate, yet, perhaps not the best measure. The '''Range = max - min'''. For example, for the Long Jump data, the range is calculated by:
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<center><math>Range = 106 – 60 = 46</math></center>. Note that the range is only sensitive to the extreme values of a sample and ignores all other information. So, two completely different distributions may have the same range.
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* TBD
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===Variance and Standard Deviation===
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The logic behind the variance and standard deviation measures is to measure the difference between each observation and the mean (i.e., dispersion). The deviation of the i-th measurement from the mean is defined by <math>(y_i - \overline{y})</math>.
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===Computational Resources: Internet-based SOCR Tools===
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Does the average of these deviations seem like a reasonable way to find an average deviation for the sample or the population? No, because the sum of all deviations is trivial:
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* TBD
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<center><math>\sum_{i=1}^n{(y_i - \overline{y})}=0.</math></center>
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To solve this problem we employ different versions of the '''mean absolute deviation''':
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<center><math>\sum_{i=1}^n{|y_i - \overline{y}|}.</math></center>
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In particular, the '''variance''' is defined as:
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<center><math>\sum_{i=1}^n{|y_i - \overline{y}|^2}.</math></center>
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And the '''standard deviation''' is defined as:
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<center><math>\sqrt{\sum_{i=1}^n{|y_i - \overline{y}|^2}}.</math></center>
===Examples===
===Examples===
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<hr>
<hr>
===References===
===References===
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* TBD
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* [http://www.stat.ucla.edu/%7Edinov/courses_students.dir/07/Fall/STAT13.1.dir/STAT13_notes.dir/lecture02.pdf Lecture notes on EDA]
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Revision as of 02:37, 28 January 2008

Contents

General Advance-Placement (AP) Statistics Curriculum - Measures of Variation

Measures of Variation and Dispersion

There are many measures of (population or sample) variation, e.g., the range, the variance, the standard deviation, mean absolute deviation, etc. These are used to assess the dispersion or spread of the population.

Suppose we are interested in the long-jump performance of some students. We can carry an experiment by randomly selecting 8 male statistics students and ask them to perform the standing long jump. In reality every student participated, but for the ease of calculations below we will focus on these eight students. The long jumps were as follows:

Long-Jump (inches) Sample Data
74 78 106 80 68 64 60 76

Range

The range is the easiest measure of dispersion to calculate, yet, perhaps not the best measure. The Range = max - min. For example, for the Long Jump data, the range is calculated by:

Failed to parse (lexing error): Range = 106 – 60 = 46
. Note that the range is only sensitive to the extreme values of a sample and ignores all other information. So, two completely different distributions may have the same range.

Variance and Standard Deviation

The logic behind the variance and standard deviation measures is to measure the difference between each observation and the mean (i.e., dispersion). The deviation of the i-th measurement from the mean is defined by (y_i - \overline{y}).

Does the average of these deviations seem like a reasonable way to find an average deviation for the sample or the population? No, because the sum of all deviations is trivial:

\sum_{i=1}^n{(y_i - \overline{y})}=0.

To solve this problem we employ different versions of the mean absolute deviation:

\sum_{i=1}^n{|y_i - \overline{y}|}.

In particular, the variance is defined as:

\sum_{i=1}^n{|y_i - \overline{y}|^2}.

And the standard deviation is defined as:

\sqrt{\sum_{i=1}^n{|y_i - \overline{y}|^2}}.

Examples

Computer simulations and real observed data.

  • TBD

Hands-on activities

Step-by-step practice problems.

  • TBD

References




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