Continuous Random Variable question

In summary, the conversation discusses a train that arrives at a station at 0820 hours and the probability that it arrives after a certain time denoted by X. It is given by a piecewise function with a constant k. The conversation also talks about finding the value of k, relating the cumulative distribution function to the probability density function, and determining the most probable arrival time of the train. The conversation also discusses a problem in which the signs in an equation seem to be swapped, but it is found that the equation is incorrect. The correct expansion for (3 + 2x)²(4 - x) is -4x³ - 4x² - 39x + 36.
  • #1
PuzzledMe
34
0

Homework Statement


A train is scheduled to arrive at the station at 0820 hours every morning and the amount of minutes that the train arrives after 0820 hours is denoted by X. The probability that the train arrives more than x minutes after 0820 hours is given by

{ 1 for x ≤ [(-3)/2],
{ (8/14641) [6(x^4) - 8x³ - 117(x²) - 216x + k) for [(-3)/2] ≤ x ≤ 4,
{ 0 for x ≥ 4,

where k is a constant.

Show that,

1) k = 1712
ii) The probability density function, f, of X is f(x) = (48/14641) (3 + 2x)²(4 - x) for [(-3)/2] ≤ x ≤ 4, and sketch the graph of f.
iii) the most probable arrival time of the train is 10 seconds after 0822 hours.

Find the probability that the train arrives before 0820 hours.

Problem statement is underlined. Having problems to prove this.

Homework Equations



F(x) = ∫ f(x) dx
Question relating to cumulative distributive function. Part ii requiring to relate cumulative distributive function to probability density function.

The Attempt at a Solution



CRV_t44_q1_1.png

CRV_t44_q1_2.png



Why I'm stuck =>
(4x³ - 4x² - 39x - 36) ≠ (3 + 2x)²(4 - x)
(3 + 2x)²(4 - x) = (-4x³) - 4x² - 39x + 36

Stuck after that as the positive and negative signs seems swapped. Checked again and nothing seems to change.

Is this due to a faulty question or I'm missing out something vital?
 
Last edited:
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  • #2
PuzzledMe said:
Why I'm stuck =>
(4x³ - 4x² - 39x - 36) ≠ (3 + 2x)²(4 - x)
(3 + 2x)²(4 - x) = (-4x³) - 4x² - 39x + 36

Stuck after that as the positive and negative signs seems swapped. Checked again and nothing seems to change.

Hi PuzzledMe! :smile:

You can check which is right by putting x = 4: that should give 0.

(4x³ - 4x² - 39x - 36) = 256 - 64 - 156 - 36 = 0
(-4x³) - 4x² - 39x + 36 = -256 -64 -156 + 36 ≠ 0.

So your expansion of (3 + 2x)²(4 - x) must be wrong!

And it is … try again! :smile:
 
  • #3
Thanks tim for the tireless response, I've solved it from here, sorry about the late reply but I owe you one!
 

FAQ: Continuous Random Variable question

What is a continuous random variable?

A continuous random variable is a type of numerical variable in statistics that can take on any value within a certain range. It is measured on a continuum, meaning there are an infinite number of possible values between any two points. Examples of continuous random variables include height, weight, and time.

How is a continuous random variable different from a discrete random variable?

A discrete random variable can only take on specific, distinct values, while a continuous random variable can take on any value within a certain range. For example, the number of children in a family is a discrete random variable, while the weight of a person is a continuous random variable.

What is the probability distribution for a continuous random variable?

The probability distribution for a continuous random variable is described by a continuous probability distribution function, which assigns probabilities to intervals of values rather than specific values. This function is represented by a curve, and the area under the curve represents the probability of a certain range of values occurring.

How is the mean and variance calculated for a continuous random variable?

The mean of a continuous random variable is calculated by multiplying each possible value by its probability and summing all the products. The variance is calculated by subtracting the mean from each possible value, squaring the differences, multiplying by the corresponding probabilities, and then summing all the products.

Can a continuous random variable take on any value?

In theory, yes, a continuous random variable can take on any value within a certain range. However, in practice, due to limitations in measurement and precision, there may be a finite number of possible values for a continuous random variable.

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