## AP Statistics 12

AP Statistics Course Overview

Big Ideas

 Variation and DistributionThe distribution of measures for individuals within a sample or population describes variation. The value of a statistic varies from sample to sample. How can we determine whether differences between measures represent random variation or meaningful distinctions? Statistical methods based on probabilistic reasoning provide the basis for shared understandings about variation and about the likelihood that variation between and among measures, samples, and populations is random or meaningful. Pattern and UncertaintyStatistical tools allow us to represent and describe patterns in data and to classify departures from patterns. Simulation and probabilistic reasoning allow us to anticipate patterns in data and to determine the likelihood of errors in inference. Data-Based Predictions, Decisions, and Conclusions Data-based regression models describe relationships between variables and are a tool for making predictions for values of a response variable. Collecting data using random sampling or randomized experimental design means that findings may be generalized to the part of the population from which the selection was made. Statistical inference allows us to make data-based decisions.

Introduction

AP Statistics is an introductory college-level statistics course that introduces students to the major concepts and tools for collecting, analyzing, and drawing conclusions from data. Students cultivate their understanding of statistics using technology, investigations, problem solving, and writing as they explore concepts like variation and distribution; patterns and uncertainty; and data-based predictions, decisions, and conclusions.

Where does this course fit?

• Pre-requisite: Pre-Calculus 11 recommended
• An optional AP exam in May (requires exam fee) where a score of 4 or 5 may be used as university course credit.

Course Materials

• Graphing calculator is recommended
• Graphing calculator is required if you are writing the AP exam

Brief Outline

 Unit Description Exploring One-Variable Data We will learn to talk about data in real-world contexts. Variability in data may seem to suggest certain conclusions, but not all variation is meaningful. Statistics allows us to develop shared understandings of uncertainty and variation. Exploring Two-Variable Data We will explore relationships in two-variable categorical or quantitative data sets, and use graphical and numerical methods to investigate an association between two categorical variables. Collecting Data In this unit, we will learn important principles of sampling and experimental design. Probability, Random Variables, and Probability Distributions Probabilistic reasoning allows statisticians to quantify the likelihood of random events over the long run and to make statistical inferences. Simulations and concrete examples can be used to understand the abstract definitions and calculations of probability. Sampling Distributions This unit applies probabilistic reasoning to sampling, introducing sampling distributions of statistics that will be used for inferences. Inference for Categorical Data: Proportions This unit introduces statistical inference. We will analyze categorical data to make inferences about binomial population proportions, construct and interpret confidence intervals, and perform significance tests to evaluate claims about population proportions. Inference for Quantitative Data: Means We will analyze quantitative data to make inferences about population means. We will also learn how and why conditions for inference with proportions and means are similar and different. Inference for Categorical Data: Chi-Square We will make connections between frequency tables, conditional probability, and calculating expected counts. The chi-square statistic is introduced to measure the distance between observed and expected counts relative to expected counts. Inference for Quantitative Data: Slopes We will learn how to construct confidence intervals for and perform significance tests about the slope of a population regression line when appropriate conditions are met.

Assessment Percentage Breakdown

 Assessment Type Percentage of the Course Assignments 45% Quizzes 15% Midterm exam 20% Final exam 20%

You have up to a year to complete your course.