CHAPTER 5 INFERENCE- Draw conclusions from evidencep pn( )1CHAPTER 5 INFERENCE- Draw conclusions from evidenceDraw conclusion about population based on a sample- how certain are you that the conclusions are right?Distribution of a statistic a function of sample data mean, smallest, 25th percentile, 25*sqrt(mean)5.1 Sampling Distributions for Counts and ProportionsBinomial data B(n,p)1) A sample size of n independent observations2) Either a success or a failure (Yes or No) (Good or Bad)3) Probability of success is p- What is the difference between a proportion and a mean?- What is the sample proportion ( p )- What is the mean (-p ) and standard deviation (-p ) of p? - When can you use the normal approximation? If np >10 and n(1-p) > 10 then pˆ~ N(p, )Can also approximate distribution of binomial counts Continuity CorrectionPage 1 of 25.2 Sampling Distribution of a Sample Mean is the mean of a SRS of size n- Mean (- ) and standard deviation (- ) of - Central Limit Theorem (p. 397)Draw a SRS of size n from any population with mean - and standards deviation -. When n is large, the sampling distribution of the sample mean, , is approximately normal:is approximately ~ N( -x , )It can be dangerous to assume your data is normally distributed. However the Central Limit Theorem and Normal Approximation for Proportions allows us to assume normality (for means and proportions) if n is large enough.http://www.gen.umn.edu/research/stat_tools/ Page 2 of
View Full Document