ADMS 2320 Lecture Notes - Lecture 6: Standard Deviation, Sampling Distribution, Central Limit Theorem
15 Sep 2016
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Central limit theorem as n gets larger, distribution gets tighter or narrower and distribution starts to look more bell-shaped. Example 9. 1: x is the continuous random variable of interest, x is amount of ounces we put in bottle, ch. 8 has only one element; ch. There are requirements for the formula, np > 5 and n(1-p) > 5. Even if it doesn"t pass required condition check, that is warning for user that there was not a big sample size; doesn"t mean you don"t finish your question. You know it is nominal because the only option is yes or no. Can"t extract standard deviation and mean, you know that you are dealing with nominal data and the questions asked feel different. Sampling distribution of the difference between two means. There is no connection, both samples are independent. The sample size of 30 or more, the distribution is approximately normal; you only need this if it not given to you in the question.
