Sampling Distributions The Central Limit Theorem is commonly referred to as the Statistician's Full Employment Act as it is the basis for much of what is done in statistics. This theory is at the core of many methods and analyses. Read “Sampling Distributions” in Chapter 5. The applet is found on the CD in the back of the textbook and in Tools for Success. This applet is designed to better help you understand the Central Limit Theorem. •Begin by running the simulation using n = 30 and N = 10 for a uniform, a bell-shaped, and a skewed distribution. ◦Compare the means from the first section (population) and third section (distribution of sample means). ◦Note the shape of the distribution of sample means (green graph in third section). ◦Evaluate if the results are what you expected for each distribution. •Run the simulation again using n = 30 and N = 1000 for the same distributions, paying attention to the green graph (distribution of sample means). ◦Compare the means for the skewed distribution again and note any changes. ◦Describe the shape of the distribution of sample means for each distribution. •Discuss how this applet increased your understanding of the Central Limit Theorem.http://media.pearsoncmg.com/ph/esm/esm_larson_les4e_09/applets/sampledist.html
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