AP Statistics Curriculum 2007

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Contents

This is an Outline of a General Advance-Placement (AP) Statistics Curriculum

Outline

This is an Internet-based E-Book for advance-placement (AP) statistics educational curriculum. The e-book is initially developed by the UCLA Statistics Online Computational Resource (SOCR), however, any statistics instructor, researcher or educator is encouraged to contribute to this effort and improve the content of these learning materials.

Every 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.

Uses and Abuses of Statistics

TBD

Design of Experiments

TBD

Statistics with Tools (Calculators and Computers)

TBD

Describing, Exploring, and Comparing Data

Summarizing data with Frequency Tables

TBD

Pictures of Data

TBD

Measures of Central Tendency

TBD

Measures of Variation

TBD

Measures of Shape

TBD

Graphs & Exploratory Data Analysis

Probability

Fundamentals

TBD

Addition & Multiplication Rules

TBD

Probabilities Through Simulations

TBD

Counting

TBD

Probability Distributions

Random Variables

TBD

Bernoulli & Binomial Experiments

TBD

Geometric, HyperGeometric & Negative Binomial

TBD

Poisson Distribution

TBD

Normal Probability Distributions

The Standard Normal Distribution

TBD

Nonstandard Normal Distribution: Finding Probabilities

TBD

Nonstandard Normal Distributions: Finding Scores (critical values)

TBD

Relations Between Distributions

The Central Limit Theorem

TBD

Law of Large Numbers

TBD

Normal Distribution as Approximation to Binomial Distribution

TBD

Poisson Approximation to Binomial Distribution

TBD

Binomial Approximation to HyperGeometric

TBD

Normal Approximation to Poisson

TBD

Estimates and Sample Sizes

Estimating a Population Mean: Large Samples

TBD

Estimating a Population Mean: Small Samples

TBD

Estimating a Population Proportion

TBD

Estimating a Population Variance

TBD

Hypothesis Testing

Fundamentals of Hypothesis Testing

TBD

Testing a Claim about a Mean: Large Samples

TBD

Testing a Claim about a Mean: Small Samples

TBD

Testing a Claim about a Proportion

TBD

Testing a Claim about a Standard Deviation or Variance

TBD

Inferences from Two Samples

Inferences about Two Means: Dependent Samples

TBD

Inferences about Two Means: Independent and Large Samples

TBD

Comparing Two Variances

TBD

Inferences about Two Means: Independent and Small Samples

TBD

Inferences about Two Proportions

TBD

Correlation and Regression

Correlation

TBD

Regression

TBD

Variation and Prediction Intervals

TBD

Multiple Regression

TBD

Multinomial Experiments and Contingency Tables

Multinomial Experiments: Goodness-of-Fit

TBD

Contingency Tables: Independence and Homogeneity

TBD

Statistical Process Control

Control Charts for Variation and Mean

TBD

Control Charts for Attributes

TBD




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