STA 309 Chapter Notes - Chapter 11: Central Limit Theorem, Standard Deviation, Sampling Distribution
Document Summary
Chapter 11: confidence intervals and hypothesis tests for means. Central limit theorem (clt): the sampling distribution of any mean becomes normal as the sample size grows. The larger the sample, the better the approximation. Means have smaller standard deviations than individuals. Confidence intervals for means: me t-distributions. Degrees of freedom: family of related distributions that depend on a parameter. As degrees of freedom increase, t-models look more normal. Few degrees of freedom narrower peak and fatter tails. Mean: t = ( - ) / se( ) Standard error (se): estimate of standard deviation of a sampling distribution n. One-sample t-interval for population mean: t*n-1 se( ) We are ___% confident that the mean ________ is between ____ and. Margin of error: me = t*n-1 se( ) Critical value t*n-1 depends on confidence level and number of degrees of freedom. H0: = 0 tn-1 = ( - 0) / se( )
