Statistics are used everywhere from fast food businesses ordering hamburger patties to insurance companies setting rates to predicting a student’s future success by the results of a test. Students will become familiar with the vocabulary, method, and meaning in the statistics which exist in the world around them. This is an applied course in which students actively construct their own understanding of the methods, interpretation, communication, and application of statistics.  Each unit is framed by enduring understandings and essential questions designed to allow students a deep understanding of the concepts at hand rather than memorization and emulation. Students will also complete several performance tasks throughout the year consisting of relevant, open-ended tasks requiring students to connect multiple statistical topics together.  The TI-83+/84 OR 89 calculator and computers will be used to explore the world of data and the patterns which can be found by analyzing this information as well as statistical relationships.  General topics of study include "exploring data," "planning and design of a study," anticipating patterns," and "statistical inference."

Algebra II

Segment 1

• Dotplots, stemplots (back-to-back stemplots), histograms, cumulative frequency plots, and parallel boxplots
• Center, shape, spread, clusters, gaps, outliers and other unusual features
• Position using quartiles, percentiles, and standardized (z) scores
• Boxplots (and modified) with the five number summary
• Center and spread both within a group and between groups
• Position of different distributions using standardization
• Correlation and linearity
• Least-squares regression lines
• Transformations to achieve linearity (logarithmic and power)
• Marginal and joint frequencies for two-way tables
• Conditional relative frequencies and determine association
• Distributions in bar charts and residual plots
• Populations, samples, and random selection
• Sources of bias in sampling and surveys (undercoverage, voluntary response, including confounding variables, the placebo effect, and blinding)
• Sampling methods (simple random sampling, stratified random sampling, and cluster sampling)
• Treatments, control groups, experimental units, random assignments, and replication
• Completely randomized designs
• Different experimental designs (randomized block design, matched pairs design)
• Generalize results from collected data
• Probability models
• Long-run relative frequencies
• Law of Large Numbers
• Independence and disjoint
• Conditional probability
• Mean and standard deviation for sums and differences of independent random variables
• Binomial and Geometrical distribution, finding the mean and standard deviation
• Properties of the normal distribution as a model for measurements
• Sampling distribution of a sample proportion and sample mean
• Central Limit Theorem
• Sampling distribution of a difference between two sample proportions and means

Segment Two

• Conduct significance tests
• Probabilities in Type I, Type II errors, and Power
• Confidence intervals and significance tests of means (both 1 sample and 2 sample)
• Sample size for a desired margin of error
• Confidence intervals and significance tests of proportions (both 1 sample and 2 sample)
• Determine sample size for a desired margin of error
• Chi-squared goodness of fit and chi-squared test of independence
• Assumptions for inference for regression or a linear regression test
• Conduct significance tests for linear regressions
• Useful language for symbolically modeling and thus simplifying and analyzing our world
• Mathematics is a logical and objective means of analyzing and solving problems
• Effective communication of mathematics is essential to its application
• Analysis of data makes use of graphical and numerical techniques to study patterns and departures from patterns
• Data must be collected according to a well-developed plan if valid information is to be obtained
• Probability is the tool used for anticipating what the distribution of data should look like under a given model
• Statistical inference guides decision making

Besides engaging students in challenging curriculum, FLVS guides students to reflect on their learning and to evaluate their progress through a variety of assessments. Assessments can be in the form of self-checks, practice lessons, multiple choice questions, projects, essays, oral assessments, and discussions. Instructors evaluate progress and provide interventions through the variety of assessments built into a course, as well as through contact with the student in other venues.

College Board has authorized FLVS to use the AP designation.  AP and Advanced Placement are registered trademarks of The College Board.