Analyzing a Distribution Function

In summary, the conversation discusses the distribution function of a real random variable X and how to calculate probabilities using this function. The function is given and plotted, and questions are asked about specific probabilities such as P( X > 1/2 ), P( 2 < X ≤ 4 ), P( X < 3 ), and P( X = 1 ). It is mentioned that for P(X > x) and P(X < x), the formula 1 - P(X ≤ x) can be used.
  • #1
twoski
181
2

Homework Statement



The distribution function of a real random variable X is given:

F(x) =

0 : x < 0
x/2 : 0 ≤ x < 1
2/3 : 1 ≤ x < 2
11/12 : 2 ≤ x < 3
1 : 3 ≤ x

(a) Plot this distribution function.
(b) What is P( X > 1/2 ) ?
(c) What is P( 2 < X ≤ 4 ) ?
(d) What is P( X < 3 ) ?
(e) What is P( X = 1 ) ?

The Attempt at a Solution



Plotting was easy, but now I'm just unsure of my answers to the others.

P( 2 < X < 4 ) = 1 - 2/3 = 1/3
P( X = 1 ) = 2/3
P( X > 1/2 ) = ? This one confuses me.
P( X < 3 ) = F(0) + F(1) + F(2) ? Not sure if i need to be subtracting something here since this is wrong.
 
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  • #2
For (b), use the fact that ##P(X > x) = 1 - P(X \le x)##.
 
  • #3
Ah, so it would essentially be 1 - P(x ≤ 1/2) which translates to 1 - 0.25 = 0.75, right?
 
  • #4
For question (d) would i just do the opposite of your formula and subtract 1 from P( X >= x )?
 

FAQ: Analyzing a Distribution Function

What is a distribution plot?

A distribution plot is a visual representation of the distribution of data values in a dataset. It shows how the data is spread out and how frequently each value occurs.

Why is it important to plot a distribution?

Plotting a distribution allows you to quickly and easily understand the characteristics of a dataset, such as its central tendency, variability, and shape. This can help identify patterns, anomalies, and outliers in the data.

How do you create a distribution plot?

To create a distribution plot, you first need to choose a suitable plot type, such as a histogram, box plot, or density plot. Then, you need to select the appropriate variables and adjust the plot parameters to accurately represent the data distribution.

What are some common features of a distribution plot?

Some common features of a distribution plot include a title, axis labels, a legend (if applicable), and a visual representation of the data distribution, such as bars, lines, or boxes. Some plots may also include statistical measures, such as mean and standard deviation.

How can a distribution plot be used to compare datasets?

A distribution plot can be used to compare datasets by plotting them on the same graph and visually comparing their characteristics, such as their shape, central tendency, and variability. This can help identify similarities and differences between the datasets.

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