Mr. Rogers' Syllabus AP Statistics

What to Expect: Reasoning based on probability and statistics gives modern society the ability to cope with uncertainty. It has astonishing power to improve decision-making
accuracy and test new ideas. It's a key analytical tool used in education, the social sciences, and business administration and is often a required college subject for majors in those areas. Statistics is frequently used for data analysis in the sciences and forms the mathematical basis for quality control in manufacturing

AP Statistics is a college level class for students who have been highly successful in Algebra II. It covers the topics needed for the American College Board AP Statistics exam. Students passing this test may receive college credit. 

In today's world most statistical analysis is done on calculators or a computers. Students will learn how to perform statistical analysis on TI -83 calculators and Excell spreadsheets with computers as well as more traditional techniques. 

Enrichment Activity

Each student is to Review the statistics in an article published in an academic, technical, or scientific journal once per quarter. Popular magazines are not allowed. The paper reviewed must be at least 3 pages long. 

The write up should be at least two good paragraphs. The first should summarize the article's content and the second should review the statistics. All reviews are to be typewritten and have a photocopy of the first page of the article and subsequent pages with relevant statistics. 

A given article can be reviewed by only one person. Students should sign up in advance for the article they want to review. All reviews are due one week before the end of the quarter.

Mr. Rogers' AP Statistics Objectives

Standards Outline (from the College Board)

I. Exploring Data: Observing patterns and departures from patterns

A. Interpreting graphical displays of distributions of univariate data (dotplot, stemplot, histogram, cumulative frequency plot)

1. Center and spread
2. Clusters and gaps
3. Outliers and other unusual features
4. Shape

B. Summarizing distributions of univariate data

1. Measuring center: median, mean
2. Measuring spread: range, interquartile range, standard deviation
3. Measuring position: quartiles, percentiles, standardized scores (z-scores)
4. Using boxplots
5. The effect of changing units on summary measures

C. Comparing distributions of univariate data (dotplots, back-to-back stemplots, parallel boxplots)

1. Comparing center and spread: within group, between group variation
2. Comparing clusters and gaps
3. Comparing outliers and other unusual features
4. Comparing shapes

D. Exploring bivariate data

1. Analyzing patterns in scatterplots
2. Correlation and linearity
3. Least-squares regression line
4. Residual plots, outliers, and influential points
5. Transformations to achieve linearity: logarithmic and power transformations

E. Exploring categorical data: frequency tables

1. Marginal and joint frequencies for two-way tables
2. Conditional relative frequencies and association

II. Planning a Study: Deciding what and how to measure

A. Overview of methods of data collection

1. Census
2. Sample survey
3. Experiment
4. Observational study

B. Planning and conducting surveys

1. Characteristics of a well-designed and well-conducted survey
2. Populations, samples, and random selection
3. Sources of bias in surveys
4. Simple random sampling
5. Stratified random sampling

C. Planning and conducting experiments

1. Characteristics of a well-designed and well-conducted experiment
2. Treatments, control groups, experimental units, random assignments, and replication
3. Sources of bias and confounding, including placebo effect and blinding
4. Completely randomized design
5. Randomized block design, including matched pairs design

D. Generalizability of results from observational studies, experimental studies, and surveys

IV. Statistical Inference: Confirming models

A. Confidence intervals

1. The meaning of a confidence interval
2. Large sample confidence interval for a proportion
3. Large sample confidence interval for a mean
4. Large sample confidence interval for a difference between two proportions
5. Large sample confidence interval for a difference between two means (unpaired and paired)

B. Tests of significance

1. Logic of significance testing, null and alternative hypotheses; p-values; one- and two-sided tests; concepts of Type I and Type II errors; concept of power
2. Large sample test for a proportion
3. Large sample test for a mean
4. Large sample test for a difference between two proportions
5. Large sample test for a difference between two means (unpaired and paired)
6. Chi-square test for goodness of fit, homogeneity of proportions, and independence (one- and two-way tables)

C. Special case of normally distributed data

1.  t-distribution
2. Single sample t procedures
3. Two sample (independent and matched pairs) t procedures
4. Inference for the slope of least-squares regression line