Essays on Uniform Distribution

Assignment 7

1. So that we will know where 50% of his subjects are located (which is below the median), and the other 50% of his subjects are located (which is above the median). 2. > y <- rnorm(16, 0,2) > hist(y)

z=X-μσn=-0.4088-0.5216=-1.818 3. > x <- rnorm(25,0,2) > hist(x) > mean(x) [1] -0.660643 t=X-μsdn=-0.660643-0.5225≈-2.90 4. Number of degrees of freedom = 25 – 1 = 24 5. t.test(x,alternative="two.sided",mu=0.25)

One Sample t-test

data: x t = -2.5001, df = ...

Introduction

The various setbacks to developing and authenticating input distributions are well known in any institutions that deal with risk assessment and analysis. Due to all these, it has been given a considerable attention more recently. There are various strategies of estimating probability distribution from either empirical data or standard approaches, which are limit practical effectiveness especially whenever such data available are limited. Whenever such cases presents themselves, certain mechanisms and strategies are employed. Such include maximum entropy criteria which actually synthesizes distribution from priori constraints. The other mechanism is to employ the use of ‘default’ such include the triangle or ...

Assignment 8

1. U = runif(100)

Largest value, around 1.0, in the uniform distribution is not as far out to the right compared to the normal distribution. This is shown as a curve to the right in the normal qqplot for the uniform distribution which takes the range from 0 to 1. Likewise, the smallest value, around 0, in the uniform distribution is not as far out to the left compared to the normal distribution. This is shown as a curve towards the left in the same qqplot. If the uniform distribution is similar to the normal distribution, the qqplot would have display a ...