Difference between revisions of "AP Statistics Curriculum 2007"

Revision as of 20:16, 16 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.

Format

Follow the instructions in this page to expand, revise or improve the materials in this e-book.

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. How many natural processes or phenomena in real life can we describe that have an exact mathematical closed-form description and are completely deterministic? How do we model the rest of the processes that are unpredictable and have random characteristics?

Uses and Abuses of Statistics

Statistics is the science of variation, randomness and chance. As such, statistics is different from other sciences, where the processes being studied obey exact deterministic mathematical laws. Statistics provides quantitative inference represented as long-time probability values, confidence or prediction intervals, odds, chances, etc., which may ultimately be subjected to varying interpretations. The phrase Uses and Abuses of Statistics refers to the notion that in some cases statistical results may be used as evidence to seemingly opposite theses. However, most of the time, common principles of logic allow us to disambiguate the obtained statistical inference.

Design of Experiments

Design of experiments is the blueprint for planning a study or experiment, performing the data collection protocol and controlling the study parameters for accuracy and consistency. Data, or information, is typically collected in regard to a specific process or phenomenon being studied to investigate the effects of some controlled variables (independent variables or predictors) on other observed measurements (responses or dependent variables). Both types of variables are associated with specific observational units (living beings, components, objects, materials, etc.)

Statistics with Tools (Calculators and Computers)

Overview TBD

Multinomial Experiments and Contingency Tables

Multinomial Experiments: Goodness-of-Fit

Overview TBD

Contingency Tables: Independence and Homogeneity

Overview TBD

Statistical Process Control

Control Charts for Variation and Mean

Overview TBD

Control Charts for Attributes

Overview TBD

SOCR Home page: http://www.socr.ucla.edu

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 ====[[AP_Statistics_Curriculum_2007_IntroDesign | Design of Experiments]] ====
-Overview TBD
+Design of experiments is the blueprint for planning a study or experiment, performing the data collection protocol and controlling the study parameters for accuracy and consistency. Data, or information, is typically collected in regard to a specific process or phenomenon being studied to investigate the effects of some controlled variables (independent variables or predictors) on other observed measurements (responses or dependent variables). Both types of variables are associated with specific observational units (living beings, components, objects, materials, etc.)
 ====[[AP_Statistics_Curriculum_2007_IntroTools |Statistics with Tools (Calculators and Computers)]]====

Difference between revisions of "AP Statistics Curriculum 2007"

Revision as of 20:16, 16 June 2007

Contents

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

Outline

Introduction to Statistics

Describing, Exploring, and Comparing Data

Probability

Probability Distributions

Normal Probability Distributions

Relations Between Distributions

Estimates and Sample Sizes

Hypothesis Testing

Inferences from Two Samples

Correlation and Regression

Multinomial Experiments and Contingency Tables

Statistical Process Control

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