A Modesto City School Joseph A. Gregori High School 3701 Pirrone Road, Modesto, CA (209) FAX (209)

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1 A Modesto City School Joseph A. Gregori High School 3701 Pirrone Road, Modesto, CA (09) FAX (09) May 4, 016 AP Statistics Parent(s): I am very excited to have your student in AP statistics next year. I look forward to meeting and getting to know the classes. We will explore what data is, how to find it, and all the amazing things we can do with it. It will be fun. Due to the volume of the course material, I am assigning summer homework for your student. They will need to go to the book room during the last week of school or the first week of summer break to get their book. The assignment will be to read Chapters 3 and 4 and complete Assignment #3 pg 36 5, 9, 1, 14, 18, 19, 7, 39 and #4 pg , 1, 14, 18, 19,, 6, 39. Points will be given for the completion of the assignments. In other words, I want it done and I don t want them to stress about getting everything correct. We will go over the assignments when school starts, but will not go over or take notes on the chapters. I will assume they will come back to school with the basic understanding of categorical and quantitative data. We will fill in any blanks when they return. Whether this is your student s first or 0 th AP class, they have a great chance to earn some college credit next year. Statistics is interesting. It falls in the math category, but really stands alone as a unique subject. Again, it will be fun! Yours truly, Matthew Soderlund Joseph A. Gregori AP Statistics Teacher

2 AP Statistics Syllabus Instructor: Textbook: Matthew Soderlund Stats Modeling the World, AP edition, by Bock, Velleman and De Veaux, Pearson Addison Wesley, 007. Other required materials: Pencil, 3 ring binder with tab dividers, TI-83/84 Plus or TI-83/84 Silver Edition graphing calculator Course description: Statistics is the art and science of collecting, organizing, analyzing, and drawing conclusions from data. In AP Statistics, we will focus on four major themes: exploratory data analysis, designing studies, probability models and simulation, and statistical inference. AP Statistics is designed as the equivalent of a one-semester, introductory statistics course. In order to get the maximum number of colleges and universities to grant credit and/or placement for high scores on the AP Statistics exam, the College Board has crafted a syllabus that includes all topics found in nearly any college introductory statistics class. As a result, the high school course covers more topics in greater depth than any single equivalent college course. In this course, students develop strategies for collecting, organizing, analyzing, and drawing conclusions from data. Students design, administer, and tabulate results from surveys and experiments. Probability and simulations aid students in constructing models for chance phenomena. Sampling distributions provide the logical structure for confidence intervals and hypothesis tests. Students use a TI-83 graphing calculator, statistical software output, and Webbased java applets and activities to investigate statistical concepts. To develop effective statistical communication skills, students are required to prepare frequent written and oral analyses of real data. Course goals: (1) To help you become an educated consumer of data and statistical claims. () To introduce you to the practice of doing statistics. Along the way, I hope you will see the many and varied applications of statistics in medicine, business, psychology, environmental science, and other important fields. You must learn to communicate your thinking effectively and efficiently. I ll provide the practice. (3) To prepare you to take the AP Statistics exam in May. Inside the Classroom: Since AP Statistics places equal importance on the accuracy of your statistical methodology and the quality of your statistical communication, I will design investigations and assignments that allow you to develop your skills in both these areas. There will be frequent writing assignments based on real data, statistical analyses, and research studies. I will lecture very selectively. My preferred method is to provide you with data to examine, simulations to perform, and guided activities to tackle that will deepen you understanding of key techniques and concepts. In short, I expect you to participate actively in all course activities.

3 Outside the Classroom: I assign homework every night. You are expected to attempt every problem I assign conscientiously and deliberately. Following a major test, you will sometimes receive a cumulative review assignment. Once or twice this year, you will collaborate with your peers on a long term project. Technology: The graphing calculator offers you a variety of tools for entering, storing, sharing, displaying, analyzing, simulating and comparing sets of data. We will use the TI s frequently! The World Wide Web offers interactive java applets, data sources, and sites with a variety of statistical information. We will even view a few video clips from the PBS series Against All Odds: Inside Statistics and Decisions through Data that were produced in the early 1990 s. Technology is an integral component of this class. Course Content Textbook Correlation Unit 1: Exploring and Understanding Data Displaying and Describing Categorical Data Frequency tables; the area principle; bar charts; pie charts; contingency tables; conditional distributions; segmented bar charts; Simpson s paradox Displaying Quantitative Data Histograms; stem-and-leaf displays; dotplots; shape, center, and spread; comparing distributions; timeplots Skill: Making a histogram on the calculator Describing Distributions Numerically Median, IQR, and 5-number summary; making and comparing boxplots; mean and standard deviation; variability; determining which summary statistics to use when The Normal Model Standardizing with z-scores; how shifting and rescaling data effect shape, center, and spread; rule; z-scores for percentiles; normal probability plots; assessing normality Skill: Finding normal percentiles using the calculator 1 days Ch3 Ch4 Ch5 Ch6 Unit : Exploring Relationships Between Variables Scatterplots, association, and correlation Describing scatterplots; explanatory vs. response variable; properties of correlation Skill: Making a scatterplot using the calculator Linear Regression The linear model; residuals; least squares regression line (LSRL); interpreting correlation; r - the variation accounted for; properties of r ; interpreting the slope and y-intercept and r in context; Properties of the LSRL - b r sy sx; x, y on LSRL Skills: Calculator discovery of LSRL properties; computing residuals & making residual plots on the calculator Regression Wisdom Subsets within data; prediction vs. extrapolation; outliers, leverage, and influential points; lurking variables and causation; summary values less variable than individual values Re-expressing Data Straightening relationships; goals of re-expression; the ladder of powers; power models log x, log y transformations; exponential models log y transformation; choosing the best model residuals and r Skill: Transformation and regression models on the calculator 3 days Ch7 Ch8 Ch9 Ch10

4 Unit 3: Gathering Data Understanding Randomness Making and conducting simulations Skill: Using random digits and using the calculator to help carry out simulations Obtaining Good Samples Simple random sample (SRS); stratified sampling; cluster sampling, systematic sampling, multi-stage sampling Sampling - sample size; census; populations and parameters vs. samples and statistics; sampling badly voluntary response; convenience sampling; Designing and Implementing Surveys Questions: wording, type, order; administration methods; response bias; undercoverage and nonresponse bias Experiments and Observational Studies observational studies vs. randomized comparative experiments; s; control treatments; blinding; placebos; blocking; factors; confounding variables vs. lurking variables Basics of Experimental Design Subjects, factors, treatments, explanatory & response variables, placebo effect, blinding; completely randomized design (CRD); diagrams Principles of Experimental Design control, random assignment, replication More Advanced Experimental Designs Multi-factor experiments; block designs; why block?; difference between blocking and stratifying; matched pairs design Unit 4: Randomness and Probability Basic Probability Concepts Probability as long-run relative frequency; randomness; legitimate probability models; sample spaces, outcomes, events; law of large numbers Basic Probability Rules Addition rule for disjoint events; complement rule; something has to happen rule; Probability Rules General addition rule, Venn diagrams, union and intersection; general multiplication rule, definition of independence; conditional probability, tree diagrams; disjoint vs. independent Random Variables Discrete vs. continuous; Discrete Random Variables expected value and standard deviation Rules for Means and Variances linear transformations of a single variable, linear combinations of random variables, independence Continuous Random Variables Combining normal random variables, calculating probabilities Binomial and Geometric Random Variables Bernoulli trials; probability density function (pdf) vs. cumulative density function (cdf) Geometric Distributions X = # of trials up to and including 1 st success; calculating geometric probabilities; expected value of geometric random variable; Binomial Distributions X = # of successes; calculating binomial probability; finding mean and standard deviation for a binomial random variable Normal Approximation Estimating binomial probabilities with normal calculations Skill: Geometric and Binomial distributions on the calculator 15 days Ch11 Ch1 Ch13 17 days Ch14 Ch15 Ch16 Ch17

5 Unit 5: From the Data at Hand to the World at Large Sampling Distribution Models - Moving towards inference; definition of sampling distribution; standard error Sampling Distributions of ˆp - Mean and standard deviation of sampling distribution; normal approximation; assumptions and conditions SRS, sample is < 10% of population, success/failure condition Sampling Distributions of x - Mean and standard deviation of sampling distribution; Central Limit Theorem (CLT); assumptions and conditions SRS, independence, 10% condition, large enough sample condition Confidence Intervals for Proportions confidence intervals to estimate a population proportion, p Estimating an Unknown Parameter The idea of a confidence interval; connection with sampling distributions; margin of error; critical values Confidence Interval Considerations Changing confidence level; interpreting CI vs. interpreting confidence level; determining sample size; assumptions and conditions independence, SRS, 10% condition, success/failure condition Skill: Calculate a one-proportion confidence interval on the calculator Testing Hypotheses About Proportions Significance tests with the inference toolbox Tests of Significance Underlying logic of significance tests; stating hypotheses; one tailed vs. two-tailed tests; P-values vs. fixed significance levels; Skill: One proportion z-test on calculator More About Tests Definition of statistically significant ; significance level, critical value; Type I & II Errors, Power Type I & II error in context; connection between power and Type II error Estimating the Difference Between Population Proportions Testing a Claim about the Difference Between Population Proportions Using the pooled proportion as an estimate Skill: Two-proportion inference on the calculator 17 days Ch18 Ch19 Ch0 Ch1 Ch Unit 6: Inference about Population Means Statistical Inference for Mean - Describing sampling distributions of sample means using a model selected from the t-distributions based on degrees of freedom; variability in sample means is the standard error; margin of error for a confidence interval; testing hypotheses about population means; checking assumptions Skill: Performing t procedures on the calculator Statistical Inference to Compare the Means of Two Independent Groups - t-models; checking assumptions; standard error for the difference between two means; twosample t intervals and two-sample t-tests Skill: Performing two-sample t procedures on the calculator different df Paired Samples and Blocks Matched pairs vs. two independent samples 11 days Ch3 Ch4 Ch5

6 Unit 7: Inference When Variables are Related Chi-Square Goodness of Fit Test The chi-square family of curves Chi-Square Test of Homogeneity Independent SRSs or randomized experiments Chi-Square Test of Association/Independence Distinguishing between homogeneity and association/independence questions Activity: M&M color distributions Inference about Linear Regression Population vs. sample regression lines Confidence Intervals and Significance Tests about - Nasty formulas; computer output; abbreviated inference toolbox Skill: Regression inference on the calculator 1 days Ch6 Ch7 AP EXAM REVIEW 0 Days Chapters 1-7 Review Practice AP Free Response Questions Mock Grading Sessions Rubric Development by Student Teams Practice Multiple Choice Questions AFTER THE AP EXAM: Students complete a Case Study Project, alone or in teams, on a topic of their choosing. Students will design, collect data, use statistical inference to find conclusions about topic. Both a written analysis and brief oral presentation are required for this project.