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 III. Anticipating Patterns: (continued) D. Sampling distributions 8. Chi-square distribution IV. Statistical Inference: B. Tests of significance Chi-square test for goodness of fit, homogeneity of proportions, and independence (one- and two-way tables)
Objectives Essential Question: Why would it be useful to have a quantitative way to test if data fits a particular distribution instead of merely relying on histograms, box plots, or normal quantile plots? Ch. 13.1 Inference for Tables  Name 2 instances when a chi-squared test can be used. Note: chi is pronounced kie (rhymes with pie). Goodness-of-fit (does data match a type of distribution?) Inference for 2-way tables (tests the Ho that there is no relationship between row and column variables) Describe the shape and range of the chi-squared distribution. Determine degrees of freedom for a goodness-of-fit chi squared calculation. ( df = n-1) Calculate the chi squared statistic. C2 = S (O - E)2 / E Perform chi squared goodness of fit hypothesis tests. Note that the hypotheses for a chi-squared test cannot readily be stated mathematically. They are as follows: Null hypothesis: The data's distribution and the reference distribution are not the same. Alternative hypothesis: The data's distribution and the reference distribution are the same. Homefun:  -- Exercises 13.9, 13.11 -- Read section 13.1	Activities Lesson 1 Key Concept:  Significance tests for distributions Warm up: What would a normal distribution look like if it were squared? Interactive Discussion: Objectives. Individual work: perform a chi-squared test on age data to determine if the population is indeed aging as a whole.
Essential Question: How can statistics be applied to genetic analysis in the real world? Describe how the chi-squared test can be used for determining if a set of data is not randomly distributed assuming that all events are equally probable.   Stats Investigation: statistical Analysis of Genome Data - computer lab using Minitab.	Lesson 2 Key Concept:  determining if data is random Interactive Discussion: Objectives. 2 person teams: see link to stats investigation
Essential Question: How can tables containing massive amounts of data be rapidly screened for relationships between the rows and columns? Ch. 13.2  Inference for Two-Way Tables Calculate expected results for tables (p.720 ). expected = (row total X column total) / (table total) Calculate chi-squared statistics for tables (p. 723). Determine the degrees of freedom for a chi-squared (p. 724). df = (rows-1)(columns-1) Perform hypothesis tests using chi-squared statistics. Be able to read chi-squared computer print outs. Homefun:  -- Exercises 13.15, 13.17, 13.18 -- Read section 13.2	Lesson 3 Key Concept:  Analysis of large tables for relationships Warm up: What would a normal distribution look like if it were squared? Interactive Discussion: Objectives. Individual work: perform a chi-squared test on a table containing drug data to determine if any of the drugs differ from the placebo. perform a chi-squared test by hand (using a calculator only for low level math) perform a chi-squared test using Minitab perform 2-sample t-tests as follow up to the chi-test

The Practice of Statistics, Yates, Moore, McCabe