Finding the Probability Mass Function for Y = 0,1,2,3...

This allows for y = 0 to be included in the pattern and the probability mass function can be derived from this equation.
  • #1
rad0786
188
0
Y = 0 ... -e^(-1/8) +1
Y = 1 ... -e^(-3/8) + e^(-1/8)
Y = 2 ... -e^(-5/8) + e^(-3/8)
Y = 3 ... -e^(-7/8) + e^(-5/8)
Y = 4 ... -e^(-9/8) + e^(-7/8)

Y = y ... -e^-(y+0.5)/4 + e^-(y-0.5)/4 for y = 1,2,3...

See the above equation, that only works for y = 1,2,3... anybody know how i could make an equation that would include y = 0? so that y = 0,1,2,3...

This comes from a stats assignment asking to derive the probability mass function. I got this pattern out of it, i just cannot get zero into it. Anybody have any ideas?
 
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  • #2
The equation can be modified to include y = 0 by subtracting 0.5 from the exponent instead of adding it. This gives the equation: Y = y ... -e^-(y-0.5)/4 + e^-(y-1.5)/4 for y = 0,1,2,3...
 

What is a probability mass function?

A probability mass function (PMF) is a mathematical function that describes the probability of a discrete random variable taking on a specific value. It assigns a probability to each possible outcome of the random variable.

How is the PMF calculated?

The PMF is calculated by dividing the number of favorable outcomes by the total number of possible outcomes. For example, if we are rolling a six-sided die and want to find the PMF for rolling a 3, we would divide the number of ways to roll a 3 (1) by the total number of outcomes (6), resulting in a PMF of 1/6.

What is the range of values for a PMF?

The range of values for a PMF is between 0 and 1, where 0 represents an impossible outcome and 1 represents a certain outcome. The sum of all the probabilities for all possible outcomes must equal 1.

Can the PMF be used for continuous random variables?

No, the PMF is only applicable for discrete random variables, where the possible outcomes are countable and finite. For continuous random variables, we use the probability density function (PDF) instead.

How is the PMF used in statistical analysis?

The PMF is used to determine the likelihood of a specific outcome occurring in a given sample or population. It is also used to calculate expected values and to compare the distribution of a random variable to a theoretical distribution.

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