HW #5
... same mean and variance. The blue curve when n = 1 is the population density. The R code for this for n = 1 is as follows. > n = 1 > gbimod(n,200,72,65) > abline(v=200 + 1.96*72/sqrt(n)) As an alternative to examining the plots one at a time on screen, the next bit of R code will create a PDF file na ...
... same mean and variance. The blue curve when n = 1 is the population density. The R code for this for n = 1 is as follows. > n = 1 > gbimod(n,200,72,65) > abline(v=200 + 1.96*72/sqrt(n)) As an alternative to examining the plots one at a time on screen, the next bit of R code will create a PDF file na ...
Sampling Distribution Proportion
... Let p be the population proportion. Then p is a fixed value (for a given population). Let p^ (“p-hat”) be the sample proportion. Then p^ is a random variable; it takes on a new value every time a sample is collected. The sampling distribution of p^ is the probability distribution of all the possible ...
... Let p be the population proportion. Then p is a fixed value (for a given population). Let p^ (“p-hat”) be the sample proportion. Then p^ is a random variable; it takes on a new value every time a sample is collected. The sampling distribution of p^ is the probability distribution of all the possible ...
Supplemental Reading – Normal Distribution
... The next topic that we need to spend time on is the concept of the normal distribution. This is a distribution that is a the foundation of how we think about much of statistical evaluation, especially within inferential statistics. So, this represents a fairly important idea. Because of its specific ...
... The next topic that we need to spend time on is the concept of the normal distribution. This is a distribution that is a the foundation of how we think about much of statistical evaluation, especially within inferential statistics. So, this represents a fairly important idea. Because of its specific ...
Central limit theorem
In probability theory, the central limit theorem (CLT) states that, given certain conditions, the arithmetic mean of a sufficiently large number of iterates of independent random variables, each with a well-defined expected value and well-defined variance, will be approximately normally distributed, regardless of the underlying distribution. That is, suppose that a sample is obtained containing a large number of observations, each observation being randomly generated in a way that does not depend on the values of the other observations, and that the arithmetic average of the observed values is computed. If this procedure is performed many times, the central limit theorem says that the computed values of the average will be distributed according to the normal distribution (commonly known as a ""bell curve"").The central limit theorem has a number of variants. In its common form, the random variables must be identically distributed. In variants, convergence of the mean to the normal distribution also occurs for non-identical distributions or for non-independent observations, given that they comply with certain conditions.In more general probability theory, a central limit theorem is any of a set of weak-convergence theorems. They all express the fact that a sum of many independent and identically distributed (i.i.d.) random variables, or alternatively, random variables with specific types of dependence, will tend to be distributed according to one of a small set of attractor distributions. When the variance of the i.i.d. variables is finite, the attractor distribution is the normal distribution. In contrast, the sum of a number of i.i.d. random variables with power law tail distributions decreasing as |x|−α−1 where 0 < α < 2 (and therefore having infinite variance) will tend to an alpha-stable distribution with stability parameter (or index of stability) of α as the number of variables grows.