AP Statistics Curriculum 2007
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Revision as of 18:48, 14 June 2007
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
- SOCR Home page: http://www.socr.ucla.edu
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