PPT-5.1 Sampling Distributions for Counts and Proportions (cont

Sample Proportions We ended last class with the following example A person has a 5 chance of winning a free ticket in a state lottery If she plays the game 12 times what is the probability she will win more than 1 free ticket. Understanding the meaning of the terminology we use.. Quick calculations that indicate understanding of the basis of methods.. Many of the possible questions are already sprinkled in the lecture slides.. Statistics. What you will learn. Be able to state the null and alternative hypotheses for testing the difference between two population proportions.. Know how to examine your data for violations of conditions that would make inference about the difference between the two population proportions unwise or invalid.. Highlights:. The law of large numbers. The central limit theorem. Sampling distributions. Formalizing the central limit theorem. Calculating probabilities associated with sample means. Two important results in inferential statistics. Parameter & Statistic. Parameter. Summary measure about population. Sample Statistic. Summary measure about sample. P. . in. . P. opulation. . &. . P. arameter. S. . in. . S. ample. . two-proportion inference tests. Chapter 22. Proportions observed in independent random samples . are. independent. Thus, we can add their variances. So…. The standard deviation of the difference between two sample proportions is . Difference Between Proportions. Sometimes we want to see if there is a significant difference between independent groups.. Control group vs. treatment group or placebo group. Men vs. women. Last year vs. this year. and Estimators. EXAMPLE . Because of rude sales personnel, a poor business plan, ineffective advertising, and a poor name, Polly Esther’s Fashions was in business only three days. On the first day 1 dress was sold, 2 were sold on the second day, and only 5 were sold on the third day. Because 1, 2, and 5 are the entire population, the mean is . Lecture Presentation Slides. Macmillan Learning ©. 2017. Chapter 5. Sampling . Distributions. 5.1 Toward Statistical Inference. 5.2 The Sampling Distribution of a Sample Mean. 5.3 Sampling Distributions for Counts and . 7. Introduction. In . a typical statistical inference problem, you want to discover one or more characteristics of a given population. .. However, it is generally difficult or even impossible to contact each member of the population.. AP Statistics. Unit 5. The Central Limit Theorem for Sample Proportions. Rather than showing real repeated samples, . imagine. what would happen if we were to actually draw many samples.. Now imagine what would happen if we looked at the sample proportions for these samples. . (resource: Barron’s AP Statistics 7. th. Edition). 1.) Graders want to give you credit.. Graders want to give you credit– help them! Make them understand . what. you are doing, . why. you are doing it, and . (resource: Barron’s AP Statistics 7. th. Edition). 1.) Graders want to give you credit.. Graders want to give you credit– help them! Make them understand . what. you are doing, . why. you are doing it, and . (resource: Barron’s AP Statistics 7. th. Edition). 1.) Graders want to give you credit.. Graders want to give you credit– help them! Make them understand . what. you are doing, . why. you are doing it, and . Objectives. In this chapter, you learn:. The concept of the sampling distribution. To compute probabilities related to the sample mean and the sample proportion. The importance of the Central Limit Theorem.