Chapter 6 Central Limit Theorem AWS
Chapter 6 Introduction to Inferential Statistics
Central Limit Theorem
Central Limit Theorem This theory that states that if a sufficiently large sample is repeatedly obtained from a population, the mean of all the samples will be approximately equal to the mean of the population. In addition if plotted, all of the sample means will follow an approximate normal distribution pattern, with all variances being approximately equal to the variance of the population divided by each sample's size. (This is called the standard error). This theory also states that there are no limitations regarding the shape of the original distribution. We can check the validity of this theorem by doing precisely what it says and noting the result.
Simulations
Population • Normal distribution • Skewed distribution • Rectangular distribution Rice Virtual Lab in Statistics http://onlinestatbook.com/stat_sim/sampling_dist/index.html
Central Limit Theorem
Central Limit Theorem This theory that states that if a sufficiently large sample is repeatedly obtained from a population, the mean of all the samples will be approximately equal to the mean of the population. In addition if plotted, all of the sample means will follow an approximate normal distribution pattern, with all variances being approximately equal to the variance of the population divided by each sample's size. (This is called the standard error). This theory also states that there are no limitations regarding the shape of the original distribution. We can check the validity of this theorem by doing precisely what it says and noting the result.
Simulations
Population • Normal distribution • Skewed distribution • Rectangular distribution Rice Virtual Lab in Statistics http://onlinestatbook.com/stat_sim/sampling_dist/index.html
