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## INTRODUCTIONThe Advanced Placement Program offers a course description and exam in statistics to secondary school students who wish to complete studies equivalent to a onesemester, introductory, non-calculus-based, college course in statistics. Statistics and mathematics educators who serve as members of the AP Statistics Development Committee have prepared the Course Description and exam to reflect the content of a typical introductory college course in statistics. The exam is representative of such a course and therefore is considered appropriate for the measurement of skills and knowledge in the field of introductory statistics. In colleges and universities, the number of students who take a statistics course is almost as large as the number of students who take a calculus course. A July 2002 article in the Chronicle of Higher Education reports that the enrollment in statistics courses from 1990 to 2000 increased by 45 percent — one testament to the growth of statistics in those institutions. An introductory statistics course, similar to the AP Statistics course, is typically required for majors such as social sciences, health sciences and business. Every semester about 236,000 college and university students enroll in an introductory statistics course offered by a mathematics or statistics department. In addition, a large number of students enroll in an introductory statistics course offered by other departments. Science, engineering and mathematics majors usually take an upper-level calculus-based course in statistics, for which the AP Statistics course is effective preparation. ## I. Exploring Data: Describing patterns and departures from patternsExploratory analysis of data makes use of graphical and numerical techniques to study patterns and departures from patterns. Emphasis should be placed on interpreting information from graphical and numerical displays and summaries
1. Center and spread 2. Clusters and gaps 3. Outliers and other unusual features 4. Shape
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
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
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
1. Frequency tables and bar charts 2. Marginal and joint frequencies for two-way tables 3. Conditional relative frequencies and association 4. Comparing distributions using bar charts ## II . Sampling and Experimentation: Planning and conducting a studyData must be collected according to a well-developed plan if valid information on a conjecture is to be obtained. This plan includes clarifying the question and deciding upon a method of data collection and analysis.
1. Census 2. Sample survey 3. Experiment 4. Observational study
1. Characteristics of a well-designed and well-conducted survey 2. Populations, samples and random selection 3. Sources of bias in sampling and surveys 4. Sampling methods, including simple random sampling, stratified random sampling and cluster sampling
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
## III. Anticipating Patterns: Exploring random phenomena using probability and simulationProbability is the tool used for anticipating what the distribution of data should look like under a given model.
1. Interpreting probability, including long-run relative frequency interpretation 2. “Law of Large Numbers” concept 3. Addition rule, multiplication rule, conditional probability and independence 4. Discrete random variables and their probability distributions, including binomial and geometric 5. Simulation of random behavior and probability distributions 6. Mean (expected value) and standard deviation of a random variable, and linear transformation of a random variable
1. Notion of independence versus dependence 2. Mean and standard deviation for sums and differences of independent random variables
1. Properties of the normal distribution 2. Using tables of the normal distribution 3. The normal distribution as a model for measurements
1. Sampling distribution of a sample proportion 2. Sampling distribution of a sample mean 3. Central Limit Theorem 4. Sampling distribution of a difference between two independent sample proportions 5. Sampling distribution of a difference between two independent sample means 6. Simulation of sampling distributions 7. t-distribution 8. Chi-square distribution ## IV. Statistical Inference: Estimating population parameters and testing hypothesesStatistical inference guides the selection of appropriate models.
1. Estimating population parameters and margins of error 2. Properties of point estimators, including unbiasedness and variability 3. Logic of confidence intervals, meaning of confidence level and confidence intervals, and properties of confidence intervals 4. Large sample confidence interval for a proportion 5. Large sample confidence interval for a difference between two proportions 6. Confidence interval for a mean 7. Confidence interval for a difference between two means (unpaired and paired) 8. Confidence interval for the slope of a least-squares regression line
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 difference between two proportions 4. Test for a mean 5. 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) 7. Test for the slope of a least-squares regression line |