# AP Statistics Curriculum 2007 Normal Std

(Difference between revisions)
 Revision as of 18:50, 14 June 2007 (view source)IvoDinov (Talk | contribs)← Older edit Revision as of 18:31, 31 January 2008 (view source)IvoDinov (Talk | contribs) Newer edit → Line 1: Line 1: ==[[AP_Statistics_Curriculum_2007 | General Advance-Placement (AP) Statistics Curriculum]] - Standard Normal Variables and Experiments== ==[[AP_Statistics_Curriculum_2007 | General Advance-Placement (AP) Statistics Curriculum]] - Standard Normal Variables and Experiments== - === Standard Normal Random Variables and Experiments=== + === Standard Normal Distrribution=== + The standard normal distribution is a continuous distribution where the + It is also a special case of the more general [[ |normal distribution]] where the mean is set to zero and a variance to one. The Standard Normal distribution is often called the ''bell curve'' because the graph of its probability density resembles a bell. + + + ===Experiments=== Example on how to attach images to Wiki documents in included below (this needs to be replaced by an appropriate figure for this section)! Example on how to attach images to Wiki documents in included below (this needs to be replaced by an appropriate figure for this section)!
[[Image:AP_Statistics_Curriculum_2007_IntroVar_Dinov_061407_Fig1.png|500px]]
[[Image:AP_Statistics_Curriculum_2007_IntroVar_Dinov_061407_Fig1.png|500px]]

## General Advance-Placement (AP) Statistics Curriculum - Standard Normal Variables and Experiments

### Standard Normal Distrribution

The standard normal distribution is a continuous distribution where the It is also a special case of the more general [[ |normal distribution]] where the mean is set to zero and a variance to one. The Standard Normal distribution is often called the bell curve because the graph of its probability density resembles a bell.

### Experiments

Example on how to attach images to Wiki documents in included below (this needs to be replaced by an appropriate figure for this section)!

### Approach

Models & strategies for solving the problem, data understanding & inference.

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### Model Validation

Checking/affirming underlying assumptions.

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### Examples

Computer simulations and real observed data.

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### Hands-on activities

Step-by-step practice problems.

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