Presentation on theme: "AP Statistics. A coin that is balanced should come up heads half the time in the long run. The French naturalist Count Buffon (1707-1788) tossed a coin."— Presentation transcript:
A coin that is balanced should come up heads half the time in the long run. The French naturalist Count Buffon ( ) tossed a coin 4040 times. He got 2048 heads. Is this evidence that Buffons coin was not balanced?
Parameters (listed as variables AND in context) Hypotheses Assumptions and Conditions Name of the test Test statistic (… my bad) Formula without the numbers plugged in Formula with the numbers plugged in Value (check this with your calculator) Obtained P-value Make decision Statement of conclusion in context
The data are an SRS This takes care of randomization The population is at least 10 times the sample size The sample size is large enough that np 0 10 and n(1-p 0 ) 10
Parameters (listed as variables AND in context) Hypotheses Assumptions and Conditions Name of the test Test statistic – formula and value Obtain P-value Make decision Statement of conclusion in context
Some boxes of a certain brand of breakfast cereal include a voucher for a free video rental inside the box. The company that makes the ceral claims that voucher can be found in 20 percent of the boxes. However, based on their experiences eating this ceral at home, a group of students believes that the proportion of boxes with vouchers is less than 0.2. This group of student purchased 65 boxes of the cereal to investigate the companys claim. The students found a total of 11 vouchers for free video rentals in the 65 boxes. Suppose it is reasonable to assume that the 65 boxes purchased by the students are a random sample of all boxes of this cereal. Based on this sample, is there support for the students belief that the proportion of boxes with vouchers is less than 0.2? Provide statistical evidence to support your answer.
To study the long-term effects of preschool programs for poor children, the High/Scope Educational Research Foundation has followed two groups of Michigan children since early childhood. One group of 62 attended preschool as 3 and 4 year olds. This is a sample from population 2, poor children who attended preschool. A control group of 61 children from the same area and similar backgrounds represents population 1, poor children with no preschool. Thus the sample sizes are n 1 = 61 and n 2 = 62. One response variable of interest is the need for social services as adults. In the past 10 years, 38 of the preschool sample and 49 of the control sample have needed social services (mainly welfare). Is there significant evidence that preschool reduces the latter need for social services?
We dont know either population proportion, only the two sample proportions. We have two ns and two p-hats We want to turn this into a single hypothesis Look at some combination of the two
What are we trying to test? What are our parameters / statistics? Hypotheses H 0 : p 1 = p 2 Theres no difference between the two proportions H a : p 1 < p 2 The first proportion is less than the second
Were trying to find if there is a difference. If there is no difference then p 1 – p 2 =0 If p 1 is greater than p 2 then p 1 – p 2 >0 Well standardize this difference. What about the standard deviation?
This is standard error (SE)
The data from each sample are an SRS This takes care of randomization Independent samples The population is at least 10 times the sample size for both samples and populations The sample size is large enough that np 1 10 and n(1-p 1 ) 10 AND np 2 10 and n(1-p 2 ) 10
P – Parameters H – Hypotheses A – Assumptions / Conditions N – Name the test T – Test statistic O – Obtain P-value M – Make decision S – State result in context