Variance of normally distributed RV

In summary, the conversation discusses the probability of a nurse being able to complete 96 journal orders within an eight hour work day with no more than 15 minutes of overtime. The time it takes for the nurse to look up a patient journal is uniformly distributed between three and seven minutes. Using the probability function and equations, it is determined that the nurse has a 55% chance of completing all the orders within the given time frame.
  • #1
Gauss M.D.
153
1

Homework Statement



The time it takes for a nurse to look up a patient journal is uniformly distributed between three and seven minutes. One morning there's 96 journal orders for the nurse to take care of.

Calculate the probability that she will get them all done during an eight hour work day, with no more than 15 minutes overtime. Assume normal distribution.

Homework Equations



Let X = number of minutes to look up one journal

The Attempt at a Solution



First, the probability function f(x) = 0.25 for 3 < x < 7

E(X) = int(0.25x dx) = 5

V(X) = E((X-5)^2) =

[itex]\int 1/4(x-5)^2 dx = 1/12(x-5)^3 _{(3 to 7)} = 4/3[/itex]

σx = 2/√3

Let Y = 96X.

E(Y) = 96E(X) = 480

This is where I'm not sure:

σy = 96σx = 111

Y = N(480,111)

P(Y < 8*60+15) = P(Y < 495)

We are 15/111 = 0.136 standard deviations above expectation.

P(Y < 495) = P(Z < 0.136) = roughly 0.55

The answer says P(Y < 495) = P(Z < 1.36) so I'm probably missing something dumb? Re-done my calcs four times now :(
 
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  • #2
Oh... σy = sqrt(96)σx

Nevermind then.
 

What is the definition of variance in a normally distributed random variable?

The variance of a normally distributed random variable is a measure of how spread out the values of the variable are from the mean. It is calculated by taking the average of the squared differences between each value and the mean.

How is the variance of a normally distributed random variable calculated?

The variance of a normally distributed random variable is calculated using the formula σ^2 = ∑(x-μ)^2 * P(x), where σ^2 is the variance, x is the value of the variable, μ is the mean, and P(x) is the probability of the value occurring.

What is the relationship between the mean and variance in a normally distributed random variable?

In a normally distributed random variable, the mean and variance are directly related. This means that if the mean increases or decreases, the variance will also increase or decrease accordingly.

How does changing the mean or standard deviation affect the variance of a normally distributed random variable?

Changing the mean or standard deviation of a normally distributed random variable will directly affect the variance. Increasing the mean or standard deviation will result in a larger variance, while decreasing the mean or standard deviation will result in a smaller variance.

Why is the variance of a normally distributed random variable important in statistical analysis?

The variance of a normally distributed random variable is important in statistical analysis because it helps to understand the spread of data around the mean. It is used in various statistical tests and can provide valuable insights into the distribution of data.

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