Distribution Function Calculations for Given Probabilities

In summary, using the given distribution function, we can find the probabilities in question. For the first question, P(X ≤ 1) is equal to 0.44. For the second question, F(1) is equal to 1/3. For the third question, P(-1 < X ≤ 3) is equal to 0.5. For the fourth question, P(2 ≤ X < 6) is equal to 1/3. For the fifth question, P(X = 5) is equal to 0. For the sixth question, P(X ≤ 6) is equal to 5/6. In this distribution function, f(X) represents the probability of getting a specific
  • #1
Leper Messiah
2
0
1.Given f(-1) = 0.15, f(1) = 0.29, f(3) = 0.21, and f(5) = 0.35, find P(X ≤ 1)

2.Given f(-1) = 0.15, f(1) = 0.29, f(3) = 0.21, and f(5) = 0.35, find F(1)

3.Given f(-1) = 0.15, f(1) = 0.29, f(3) = 0.21, and f(5) = 0.35, find P(-1 < X ≤ 3)

and the rest using this distribution function
F(x) = [0 for x < 1; 1/3 for 1 <= x < 4; 1/2 for 4 <= x < 6; 5/6 for 6 <= x < 10; 1 for x >= 10]

4.find P(2 ≤ X < 6)

5.find P(X = 5)

6.find P(X ≤ 6)

I know this is a lot, but help is really needed and I would be much appreciative!
 
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  • #2
You also need to read the rules of this forum (which you should have done already): show what you have done already on this problem so we will know what hints you need.

It also would be a good idea to clearly state the problem. Are you saying that the first 3 problems are discrete probabilities with 5 outcomes? And that the last problems are continuous probabilities? And what is the difference between f(X) and F(X)?
 

1. What is a distribution function?

A distribution function is a mathematical function that describes the probability of a random variable taking on a specific value or falling within a certain range of values. It is commonly used in statistics and probability theory to model and analyze data.

2. How is a distribution function different from a probability density function?

A distribution function describes the probability of a random variable taking on a specific value or range of values, while a probability density function describes the relative likelihood of a random variable taking on a specific value. In other words, a distribution function gives the probability of a specific outcome, while a probability density function gives the relative likelihood of all possible outcomes.

3. What are some common types of distribution functions?

Some common types of distribution functions include the normal distribution, binomial distribution, exponential distribution, and Poisson distribution. These distributions are often used to model real-world phenomena and are characterized by specific mathematical properties and shapes.

4. How is a distribution function used in data analysis?

A distribution function can be used to calculate probabilities and make predictions about a dataset. By fitting a distribution function to a set of data, we can determine the likelihood of certain outcomes and make statistical inferences about the underlying population from which the data was collected.

5. Can a distribution function be used for non-numerical data?

No, a distribution function is typically used for numerical data, as it describes the probability of a random variable taking on a specific numerical value. However, there are extensions of distribution functions, such as categorical distribution functions, that can be used for non-numerical data.

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