Mr. Rogers - AP Statistics Objectives

Syllabus	1st Quarter	2nd Quarter	3rd Quarter	4th Quarter
11 t-Test-	12 Inf for Prop	13 Chi Test	14 Regression

Unit Plan Practice Test
Latin/Greek Root Words arch--------->ancient, example: archtype;         chrono------>time, example: chronology;             -dom----------->quantity/state, example: freedom               fer-------->carry, example: transfer;               gen--------->birth, example: generate;                 luc-------->light, example lucid;                 neo--------->new, example: neonatologist;                olig--------->few, example: oligarchy;              omni--------->all, omniscient;            sym--------->together, symbol; (Statistics connection)
AP Statistics Standards IV. Statistical Inference: Confirming models A. Confidence intervals The meaning of a confidence interval Large sample confidence interval for a proportion B. Tests of significance 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 Large sample test for a proportion Large sample test for a mean Large sample test for a difference between two proportions
Objectives Essential Question: How does a confidence interval for proportions compare to one for means? Ch. 12.1 Inference for Proportions State the meaning of p-hat. A statistic representing a population proportion Calculate the mean and standard deviation of a binomial distribution. Data Type Mean Std Dev count np [np(1 - p)]^0.5 proportion p [p(1 - p) / n]^0.5 Be aware that a binomial distribution (the distribution typically used for analyzing proportions) is essentially a sampling distribution. Note that as a sampling distribution, when the sample size is large enough (see below), the distribution begins to resemble a normal distribution. When appropriate, correctly model a binomial distribution as a normal distribution if the 2 conditions shown below are met. np ≥ 10 n(1-p) ≥ 10 Create a confidence interval for a proportion based on single large  sample  (p.689). ME = Z* [p(1 - p) / n ]0.5 Perform a hypothesis test comparing a single large sample proportion (p-hat) against a know population proportion (p). Note, this is a z-test.   Homefun (formative/summative assessment):  -- 12.7, 12.9 -- Read section 12.1	Activities Lesson 1 Essential Question: How can inferences be draw for single sample proportions? Warm up (individuals): What type of distribution is most useful for evaluating surveys for voting or yes or no questions? Why does the binomial distribution start to look like a normal distribution when the sample size is large? Interactive Discussion: Objectives 1-2. D Stats Investigation (Teams of two):
Essential Question: Assuming an SRS and given equal sized margins of error, is the sample size required to survey the entire United States substantially larger than the one for conducting the same survey in Greenville SC? Ch. 12.1, 12.2 Calculate the desired sample size for a given margin of error in a proportion (p. 696). Remember that p = 50% will give the max sample size and hence most conservative estimate of the size needed. Using the equation for margin of error, solve for n. Relevance: Survey results are a ubiquitous feature of newspaper and magazine articles as well as political arguments. The above is the basic way that surveys are designed. Create a confidence interval for comparing two sample proportions (p.704). State the Ho used for comparing two sample proportions. Calculate the pooled portion of successes using both samples.   ppooled =   count of successes in both samples combined  count of observations in both samples combined            = X1 + X2 n1 + n2 Perform a hypothesis test for comparing two sample proportions (p. 708).   Homefun (formative/summative assessment): 12.11,  12.25, 12.27, 12.35 -- Read Section12.2	Lesson 2 Essential Question: How can inferences be draw for two sample proportions? Warm up questions  (individuals): What's the difference between p and p-hat? What are the two rules of thumb for modeling the binomial distribution with a normal distribution?