AP Statistics. Materials: Students will need a TI-83+ or TI-84+ calculator and internet access.

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1 AP Statistics Text: Yates, Daniel, David Moore and Darren Starnes, The Practice of Statistics 2 rd ed., W.H. Freeman and Company, New York aligned with College Board Indicators AP Questions are available from the College Board website Other Resources Used: 1) David E. Bock, Velleman, DeVeaux, Stats Modeling the World, Pearson Addison Wesley, New York, ) Ron Millard, Turner, Activities and Projects for Introductory Statistics Courses,2 nd edition, W.H. Freeman and Company, New York2008. ) Daniel Yates, Starnes, Moore, Statistics through Applications, W.H. Freeman and Company, New York, ) Martin Sternstein, Ph.D., Barron s AP Statistics, 4 th edition, Barron s Educational Series, New York, Course: AP Statistics Description: This is a year long class meant to be taught at the introductory college level. The course moves through four basic units including data gathering, data analysis, probability and inference. More specific skill breakdown is provided in the above pacing and standards alignment guide. Materials: Students will need a TI-8+ or TI-84+ calculator and internet access. Goals: 1) Students will learn to gather data in using appropriate methods and display that method in multiple ways. 2) Students will use technology to organize, display, simulate and perform appropriate calculations and tests within statistical problems. ) Students will learn to look at numbers with a new understanding of where those numbers came from and be more aware of both proper and improper production and use of statistical information. 4) Students will learn the essential elements of experimental design and survey methods. ) Students will understand the idea of randomness with respect to discrete and continuous data sets. ) Students will learn to draw conclusions about what the data tell them through various types of hypothesis testing.

2 College Board/ Indicator Ref. # Text Section 1.0 Exploring Data Chapters 1-4 Objectives: 1) Students will become familiar with major types of data display including bar charts, pie charts, dot plots, line plots, stem and leaf plot, boxplots and histograms. 2) Students will be able to construct data displays for quantitative variables and will use TI-84 calculators to construct histograms, boxplots and scatter plots. ) Students will be able to choose and justify appropriate summary statistics for a data set after commenting on the shape, center and spread of a distribution based on the raw data and its display. 4) Characteristics of density curves with an emphasis on Normal Distributions will be explored including standardizing data and the empirical rule. ) Students will use normal probability plots to help justify the use of a normal model. ) Students will assess linearity of bivariate data by looking at a scatter plot, residual plot and calculating the correlation coefficient. 7) Students will use technology to generate models for data Days (Time) Resources AP Questions; Vocabulary 44 In addition to the text demonstrations will be made using Fathom software in class. Essential Vocabulary: Individuals Variable (categorical and quantitative) Distribution Shape Center Spread Outlier (know types and how to test for them) number summary Mean Standard deviation Normal curve LSRL Residual Correlation Influential point Conditional Distribution Simpsons s paradox

3 including LSRL and comment on those models using the coefficient of determination and other analysis. 8) Students will use models to make predictions about the data. 9) Students will use basic functions to transform data in order to improve analytical potential. 1.1 a, b, c, d Introduction pp Displaying data-charts 1.1 pp dotplots/stemplots/histograms 1.2 a, b, c, d, e 1.2 pp. 7-4 mean/ number summary 1. a, b, c, d 1.2 pp spread-standard Deviation comparative distributions. a, b, c 2.1 pp density curves-normal dist Rule 2.2 p standardizing data/standard normal curve QUIZ #1 1.4 a, b, c.1 pp scatterplots-construction of.2 pp correlation. pp LSRL AP: 2001 #a; 2002B #; 200 #1a,b; 200 #1; 2008B #1 HW 1: p. 4- # odd AP: 200 #1a (choice of measure of center) AP: 2000 #; 2001 #1; 2004B #a; 2004 #1; 200 #1 HW 2: p.-70 # AP: 2007 #1a HW : p.90-9 AP: 1998 #a; 1999 #4a,c; 2002 #a; 2004B #a,b HW 4: p odd HW : p.1-19 AP: 1998 #2; 2000 #1; 2002B #1 HW : p AP: 200 #2 (regression line from

4 pp the role of r 2 pp residuals 1.4 e 4.1 pp transforming relationships log/ exponential models pp power models 1.4 d 4.2 pp extrapolation/lurking variables 1. a, b, c, d 4. PP two way tables/conditional distributions TEST Sampling and Experimentation Chapter Objectives: 1) Students will understand how to gather data effectively by asking the key question: What do we want to discover? 2) Students will learn to construct appropriate survey questions to answer questions about a target population. ) Students will learn to model situations through simulation techniques using TI-84 calculators and Fathom software. 4) Students will understand the difference between observational studies and experiments. ) Students will learn how to determine the effects of treatments on a response variable through experimental 2 software) AP: 200 # AP: 1998 #4 (residual plots); 1999 #c; 2002 #4; 200B #1(influential points) HW 7: p HW 8: p HW 9: p AP: 1997 #; 2004B #1 HW 10: p even 1 Essential Vocabulary: Observational Study Experiment Population Sampling Census Voluntary Response Biased SRS Probability Stratified Random Undercoverage Nonresponse Systematic Random Convenience Factor Level

5 2.1 a, b, c, d 2.2 a, b, c, d design..1 pp observation v. experiment sampling design: population, census, voluntary response, convenience, bias, SRS, PRB sampling and stratifying 2. a, b, c, d, e.2 pp designing experiments: Units, subjects, treatments, factors, levels, placebo, controls, randomization, replication, significance pp Blinding, matched pairs, block designs.1 e, 2.4. pp Simulating experiments: random digit assignment.0 Anticipating Patterns QUIZ Chapter Chapters -9 Objectives: 1) Students will explore random behavior and become familiar with the Law of Large Numbers. 2) Students will learn basic probability rules through simulation and formal methods. ) Students will move from Placebo Randomize Control Replicate Block Blinding (double) Probability Model Simulation AP: 200 #b,c; 2007 #a HW 11: p AP: 200B #a; 2007 #2 HW 12: p.0-0 AP: 1998 #; 1999 #; 2000 #; 2001 #4 (blocking); 2002 #2 (match pairs design); 2002B #; 200 #4 (randomization); 2004 #2 (blocking); 2004B #2; 200 # AP: 200 #4a,b,d; 2007 #2b HW 1: p.19-2 Part I of Project Due Essential Vocabulary: General Probability Rules Disjoint Complimentary Independent Event Outcome

6 discrete probability calculation to theoretical distribution models including the normal model, geometric model and binomial model..1 a.1 pp. 0- The idea of probability.1 c.2 pp. -48 sample space, event, prb model, Multiplication, Addition, and Complement Rules.1 c. pp Conditional Probability-General Rules.1 d 7.1 pp discrete/continuous random variables, normal distributions as PRB distributions.1 b, f 7.2 pp means and variances of random variables, Law of Large Numbers QUIZ Chapters -7.1 d 8.1 pp binomial distributions.1d 8.2 pp geometric distributions 2.2 a, b, c 9.1 pp Without replacement Discrete Continuous Expected Value Binomial Probability Geometric Probability HW 14: p. -9 AP: 1997 #; 1999 #; 200B #2; 2004 #4a HW 1: p.79-8 HW 1: p AP: 1999 #b; 2000 #b,c; 2001 #2; 2002 #; 2002B #2; 200B #; 200 #a; 2004B #c,d (normal curve); 2004 #4b,c; 200 #2 (expected values); 200 #a HW 17: p AP: 1998 #b,c,d,e; 1999 #4b; 2001 #; 2004 # (conditions for binomial setting); 200 #b,c HW 18: p HW 19: p odd

7 sampling distributions, variability, parameters, bias HW 20: p a 9.2 pp sample proportions HW 21: p b, c 9. pp.14 2 sample means, central limit theorem 4.0 Statistical Inference 4.1 c, f TEST 2 Chapters Objectives: 1) Students will use sample statistics to estimate a range of possible values for population parameters (confidence intervals). 2) Students will propose models for situations and examine observed statistics to see if the model makes sense. ) Students will learn to appropriately identify and use inference procedures to test hypothesis based on the central limit theorem including tests about proportions and means. 4) Students will learn to check underlying assumptions for tests. ) Students will know when to use t models, matched-pairs t models and how to perform tests on counted data. ) Students will identify understand and perform inferences for regression pp. 7 estimating with confidence, confidence intervals for a population mean, margin of error AP: 1998 #1 (CLT); 2004 #c,d (CLT); 2007 # HW22: p Essential Vocabulary: Hypothesis Null Type I Error Type II Error Power Significance Level Standard Error Pooled Data Confidence Level Inference Margin of Error P-Value AP: 2007 #1c HW 2: p.-8

8 4.2 a, d 10.2 pp tests of significance, alternative and null hypotheses, p-value, statistical significance, onesample z statistic 4.2a 10. pp statistical significance 4.2a 10.4 pp. 9 0 inference as decision, type I and type II errors, power QUIZ Chapters 8-10 Project Part II Due.4 g, 4.1 f, 4.2 a, d 11.1 pp inference for the mean of a population, one-sample t- statistic, t confidence intervals and tests of significance, matched pairs t procedures, robustness 4.1 g, 4.2 e 11.2 pp comparing two means, twosample problems, difference between means 4.1 d, 4.2 b 12.1 pp inference for a population proportion, confidence intervals for p 4.1 e, 4.2 c 12.2 pp comparing two proportions, confidence intervals for differences of proportions, significance tests for differences of proportions Projects Due AP: 200 #a HW 24: p.8-8 AP: 2002 #1; 2002 #a,b; 200B #; 200 #b,c,d HW 2: p AP: 2000 #2 (ttest); 200 #1c; 2004 # (confidence interval only); 2004B #b,c; 2007 #4 HW 2: p.42-4 AP: 1999 #a,b; 2000 #4; 2001 # (paired t-test or two sample z test); 2002B #a; 200B #4c; 2004B #4 (confidence interval only); 200 #; 200 #4 HW 27: p AP: 1998 #; 2002B #4; (Type I and Type II Error) HW 28: p odd AP: 2000 #; 2002 #, #c,d; 200B #b; 2004B #; 2007 # HW 29: p h, 4.2 f 1.1 pp HW 0: p

9 Chi square test for goodness of fit 4.2 f 1.2 pp inference for two-way tables, chi-square statistic, chi-square test for homogeneity of populations TEST + ( AP Exam) 4.1 h, 4.2 g 14.1 pp regression inference, confidence intervals for the regression slope Pacing based on 2 minute periods days per week with approximately 1 prep days before AP Exam. 4 AP: 1999 #2 (independence); 2002 #; 2002B #b (homogeneity); 200 # (independence); 200B #c (independence) HW 1: p.7-77 AP: 2001 #b HW 2: p