Difference between revisions of "AP Statistics Curriculum 2007"
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===Outline=== | ===Outline=== | ||
| − | Each topic discussed in | + | Each topic discussed in the SOCR AP Curricumum should contain the following subsections: |
| − | * Motivation/Problem: A real data set and fundamental challenge. | + | * '''Motivation/Problem''': A real data set and fundamental challenge. |
| − | * Approach: Strategies for solving the problem, data understanding & inference. | + | * '''Approach''': Strategies for solving the problem, data understanding & inference. |
| − | * Model Validation: Checking/affirming underlying assumptions. | + | * '''Model Validation''': Checking/affirming underlying assumptions. |
| − | * Computational Resources: Internet-based SOCR Tools (offline resources, e.g., tables). | + | * '''Computational Resources''': Internet-based SOCR Tools (offline resources, e.g., tables). |
| − | * Examples: computer simulations and real observed data. | + | * '''Examples''': computer simulations and real observed data. |
| − | * Hands-on activities: Step-by-step practice problems. | + | * '''Hands-on activities''': Step-by-step practice problems. |
===Introduction to Statistics=== | ===Introduction to Statistics=== | ||
Revision as of 10:56, 12 June 2007
Contents
- 1 This is an Outline of a General Advance-Placement (AP) Statistics Curriculum
- 1.1 Outline
- 1.2 Introduction to Statistics
- 1.3 Describing, Exploring, and Comparing Data
- 1.4 Probability
- 1.5 Probability Distributions
- 1.6 Normal Probability Distributions
- 1.7 Relations Between Distributions
- 1.8 =Law of Large Numbers
- 1.9 Estimates and Sample Sizes
- 1.10 Hypothesis Testing
- 1.11 Inferences from Two Samples
- 1.12 Correlation and Regression
- 1.13 Multinomial Experiments and Contingency Tables
- 1.14 Statistical Process Control
This is an Outline of a General Advance-Placement (AP) Statistics Curriculum
Outline
Each topic discussed in the SOCR AP Curricumum should contain the following subsections:
- Motivation/Problem: A real data set and fundamental challenge.
- Approach: Strategies for solving the problem, data understanding & inference.
- Model Validation: Checking/affirming underlying assumptions.
- Computational Resources: Internet-based SOCR Tools (offline resources, e.g., tables).
- Examples: computer simulations and real observed data.
- Hands-on activities: Step-by-step practice problems.
