In your short presentation, you will be describing an example that uses discrete probabilities or distributions. Provide an example that follows either the binomial probabilities or any discrete probability distribution, and explain why that example follows that distribution.
*Can we please not use a coin example if possible
Firstly , let us discuss about discrete probability distributions :
The probability distribution which describes the probability of occurrence of each value of a discrete random variable.
Where, discrete random variable is a random variable that has countable values . Such as a list of natural numbers.
For example, An ice- cream seller records the total amount of Vanilla , Chocolate and Strawberry flavors, he sold each day for two weeks :
Vanilla | Chocolate | Strawberry | |
Day 1 |
130 | 70 | 50 |
Day 2 | 120 | 50 | 50 |
Day 3 | 180 | 90 | 80 |
Day 4 | 190 | 100 | 40 |
Day 5 | 150 | 70 | 50 |
Day 6 | 160 | 50 | 60 |
Day 7 | 140 | 40 | 30 |
Day 8 | 170 | 80 | 30 |
Day 9 | 160 | 70 | 60 |
Day 10 | 180 | 100 | 70 |
Day 11 | 160 | 50 | 30 |
Day 12 | 190 | 50 | 40 |
Day 13 | 130 | 70 | 70 |
Day 14 | 140 | 40 | 50 |
Here, we can see that each value is a whole number and certain countable (i.e. discrete random variable) .
and Now, we are looking for it's Discrete Probability Distribution :
Number | Frequency | Probability |
120 | 1 | 0.071 |
130 | 1 | 0.071 |
140 | 2 | 0.143 |
150 | 2 | 0.143 |
160 | 3 | 0.214 |
170 | 1 | 0.071 |
180 | 2 | 0.143 |
190 | 2 | 0.143 |
Here, we can see that each value if "Number" is an example of Discrete Random Variable and
Total sum of probability is exactly equals to 1 .So, this represents Discrete Probability Distribution.
Hence , the above example follows discrete distribution.
We can take any example having discrete random variables.
In your short presentation, you will be describing an example that uses discrete probabilities or distributions....
Describe an example that uses discrete probabilities or distributions. Provide an example that follows either the binomial probabilities or any discrete probability distribution, and explain why that example follows that distribution.
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