Difference between revisions of "AP Statistics Curriculum 2007"
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===Introduction to Statistics=== | ===Introduction to Statistics=== | ||
| − | ====The Nature of Data & Variation==== | + | ====[[AP_Statistics_Curriculum_2007_IntroVar | The Nature of Data & Variation]]==== |
| + | No mater how controlled the environment, the protocol or the design, virtually any repeated measurement, observation, experiment, trial, study or survey is bound to generate data that varies because of intrinsic (internal to the system) or extrinsic (due to the ambient environment) effects. | ||
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====Uses and Abuses of Statistics ==== | ====Uses and Abuses of Statistics ==== | ||
====Design of Experiments ==== | ====Design of Experiments ==== | ||
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===Relations Between Distributions=== | ===Relations Between Distributions=== | ||
====The Central Limit Theorem ==== | ====The Central Limit Theorem ==== | ||
| − | ====Law of Large Numbers=== | + | ====Law of Large Numbers==== |
====Normal Distribution as Approximation to Binomial Distribution ==== | ====Normal Distribution as Approximation to Binomial Distribution ==== | ||
====Poisson Approximation to Binomial Distribution ==== | ====Poisson Approximation to Binomial Distribution ==== | ||
Revision as of 18:06, 13 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 Estimates and Sample Sizes
- 1.9 Hypothesis Testing
- 1.10 Inferences from Two Samples
- 1.11 Correlation and Regression
- 1.12 Multinomial Experiments and Contingency Tables
- 1.13 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: Models & strategies for solving the problem, data understanding & inference.
- Model Validation: Checking/affirming underlying assumptions.
- Computational Resources: Internet-based SOCR Tools (including offline resources, e.g., tables).
- Examples: computer simulations and real observed data.
- Hands-on activities: Step-by-step practice problems.
Introduction to Statistics
The Nature of Data & Variation
No mater how controlled the environment, the protocol or the design, virtually any repeated measurement, observation, experiment, trial, study or survey is bound to generate data that varies because of intrinsic (internal to the system) or extrinsic (due to the ambient environment) effects.
