HSS 2381 Lecture Notes - Lecture 4: Central Limit Theorem, Estimation Theory, Statistical Inference
10 Oct 2018
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It"s less ti(cid:373)e co(cid:374)su(cid:373)i(cid:374)g the(cid:374) a ce(cid:374)sus: less costly to administer then a census, possible to obtain statistical results of a sufficiently high precision based on samples. Random samples: simplest method that provides every individual an equal opportunity to be selected, objects selected independently from a table of random numbers or computer generator. Parameters vs statistcs: parameter = population, statistic = sample. Statistical estimation: statistical inference = sample to draw conclusions about population. Sampling distributions: represents the sampling mean, when you graph it if give you the sampling distributions, when the mean is weak the sd values is very high. Central limit theorem: sample size of 30 or more, population is normally distributed, standard deviation for a sample means, sd = population sd/square root of n.
