# AP Statistics Curriculum 2007 EDA Var

### From Socr

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===Measures of Variation and Dispersion=== | ===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: | |

- | + | ||

- | + | {| class="wikitable" style="text-align:center; width:75%" border="1" | |

+ | |+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: | |

+ | <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. | ||

- | + | ===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 <math>(y_i - \overline{y})</math>. | ||

- | == | + | 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: |

- | + | <center><math>\sum_{i=1}^n{(y_i - \overline{y})}=0.</math></center> | |

+ | |||

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

+ | <center><math>\sum_{i=1}^n{|y_i - \overline{y}|}.</math></center> | ||

+ | |||

+ | In particular, the '''variance''' is defined as: | ||

+ | <center><math>\sum_{i=1}^n{|y_i - \overline{y}|^2}.</math></center> | ||

+ | |||

+ | And the '''standard deviation''' is defined as: | ||

+ | <center><math>\sqrt{\sum_{i=1}^n{|y_i - \overline{y}|^2}}.</math></center> | ||

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

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<hr> | <hr> | ||

===References=== | ===References=== | ||

- | * | + | * [http://www.stat.ucla.edu/%7Edinov/courses_students.dir/07/Fall/STAT13.1.dir/STAT13_notes.dir/lecture02.pdf Lecture notes on EDA] |

<hr> | <hr> |

## 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:

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**

### 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 .

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:

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

In particular, the **variance** is defined as:

And the **standard deviation** is defined as:

### Examples

Computer simulations and real observed data.

- TBD

### Hands-on activities

Step-by-step practice problems.

- TBD

### References

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

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