Summary of Probability Distributions
For Crystal Ball, all of these distributions can be used either from Crystal Balls Gallery (defined as assumption cells), or by explicitly writing the CB. function
1
Lecture 1. Transformation of Random Variables
Suppose we are given a random variable X with density fX (x). We apply a function g to produce a random variable Y = g (X ). We can think of X as the in
Transformation and Expectation 1 Function of a random variable
Assume that X is a random variable with pmf/pdf fX , cdf FX . Denote the sample space of X by X . Then any function of X, say Y = g(X), i
Summary of Probability Distributions of Discrete Random Variables
Name and parameter(s) of the distribution Binomial Bernoulli Negative Binomial Geometric B(n,p) n =1, p nb(r, p) r = 1, p pmf(*) n p(
TRANSFORMATIONS OF RANDOM VARIABLES
1. INTRODUCTION 1.1. Denition. We are often interested in the probability distributions or densities of functions of one or more random variables. Suppose we have a
Jordan Mitchell
1.16
Work
Not work
Men
15
23
38
Women
65
28
93
Total
80
51
131
a. The missing totals were 80,93,131
b. 39%
c. 61%
d. 70%
e. 61%
f. 50%
g. 11%
h. 560 women
1.30
We cannot conclude that
Jordan Mitchell
2.6
a. 12,20
b. 8 hours
c. 11
d. 11/50 22%
2.8
a. Detroit
b. Seattle
c. Left Skewed
2.30
Animals with outliers: African & Asian elephant 660,645
The humans data point would be the cent