Fourth Quarter Project: AP Review (Period F)

Grading Criteria for AP Review:

1. Although some people will be working in pairs, each person’s score will be determined individually.

2. Altogether, AP review will count for 100 points (i.e., one test). Equal weights will be assigned to creativity (25 points), accuracy (25 points), value added to the review process for your classmates (25 points), and multiple-choice questions (25 points).

3. The first three components (creativity, accuracy, and value added) will be prorated equally between the two partners for those working in pairs. The multiple-choice component will be scored individually as described in #4.

4. Each person (not each pair, each person) will write at least four (4) multiple-choice questions from his review area. These are due no later than Friday, April 30, 1999, for everybody. However, if those who are presenting during the week of April 26 could try to have their questions turned in before class time on the day they are presenting, that would be helpful. (There is no point penalty for not turning questions in before April 30.) Be sure to include an answer key with your questions. Do not "lift" questions directly from other sources—you will learn much more if you write up your own. You may, of course, see me for examples of questions to use for inspiration. Each question should have 5 possible answers (A through E).

5. In addition to the 100 points, you will be expected (for HW scoring) to work at least 7 multiple choice and 1 free-response question each weeknight during the AP review period. Questions and answers will be handed out each day, and you will be on your own to check your answers and learn from your mistakes. You will be required to keep a daily log of your work to show the effort that you have put into this process. (Forms for this purpose will be distributed during the week of April 26.)

Presentations will begin Thursday, 4/29/99. Order will be random, and the schedule will be as shown on the Web calendar. Topic assignments are as follows:
I. Alex and Bob
II. Evan and Dan S. (one approach), and Chris H. and Corey (another approach)
III.A and additional problems: Karim and Jamey
III.B,C,D: Dan H. and Sam
IV.A, plus relevant portions of C: Stephan and Chris M.
IV.B, plus relevant portions of C: Norman and Garth

AP Statistics Course Outline
(source: http://www.collegeboard.com/ap/statistics/html/topic001.html)

I. Exploring Data: Observing patterns and departures from patterns

Exploratory 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. 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
            2. 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
            3. 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
            4. 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
            5. 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

Data 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. Overview of methods of data collection
              1. Census
              2. Sample survey
              3. Experiment
              4. Observational study
            2. 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
            3. 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
            4. Generalizability of results from observational studies, experimental studies, and surveys

III. Anticipating Patterns: Producing models using probability theory and simulation

Probability is the tool used for anticipating what the distribution of data should look like under a given model.
            1. Probability as relative frequency
              1. "Law of large numbers" concept
              2. Addition rule, multiplication rule, conditional probability, and independence
              3. Discrete random variables and their probability distributions, including binomial
              4. Simulation of probability distributions, including binomial and geometric
              5. Mean (expected value) and standard deviation of a random variable, and linear transformation of a random variable
            2. Combining independent random variables
              1. Notion of independence versus dependence
              2. Mean and standard deviation for sums and differences of independent random variables
            3. The normal distribution
              1. Properties of the normal distribution
              2. Using tables of the normal distribution
              3. The normal distribution as a model for measurements
            4. Sampling distributions
              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

IV. Statistical Inference: Confirming models

Statistical inference guides the selection of appropriate models.
            1. Confidence intervals
              1. The meaning of a confidence intervals
              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)
            2. 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)
            3. 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