Find the Variance of this distribution

In summary, the random variable X represents the mean number of hours spent in a library per week by the ten chosen students (four arts students and six science students). The variance of this random variable is calculated to be 9.76 hours, but this result does not pass the smell test as it suggests a standard deviation of 3.1 hours, which is not realistic. This is due to the confusion between the sum of two random variables and a random variable multiplied by a constant.
  • #1
thereddevils
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Homework Statement



The number of hours spent in a library per week by arts and science students in a college is normally distributed with mean 12 hours and standard deviation 5 hours for arts students and mean 15 hours and standard deviation 4 hours for science students. A random sample of four arts students and six science students is chosen. Assuming that X is the mean number if hours spent by these 10 students in a week. Calculate Var(X)


Homework Equations





The Attempt at a Solution



Let X_A and X_S be the random variable of number of hours spent by arts and science students respectively.

X=(4X_A+6X_S)/(10)

Var(X)=Var((4X_A+6X_S)/(10))

=1/100 (16 Var(X_A)+36 Var(X_s))

=1/100 (16 x 25 + 36 x 16)

=9.76

Am i correct?
 
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  • #2


Does this pass the smell test? You are indicating that the standard deviation of the mean of the ten students is like 3.1 hrs.

But suppose we just took ten arts students (who have the higher standard deviation). You should know that the deviation of the group will be [itex] \frac{5}{\sqrt{10}} \approx 1.6 [/itex]. So something is wrong!

What happened is that you got confused between two things:

Var(X + X) = 2 Var(X) (the sum of two random variables X)
Var(2X) = 4 Var(X) (a random variable X multiplied by 2)

When we pick four arts students, that's not one arts student multiplied by 4. Hence we don't get 16*Var(X_A), etc.
 

1. What is variance and why is it important in data analysis?

Variance is a statistical measure that describes how spread out the data is in a distribution. It is important in data analysis because it helps us understand the variability and diversity of the data, which can provide important insights and inform decision-making.

2. How is variance calculated?

Variance is calculated by taking the sum of squared differences between each data point and the mean of the distribution, divided by the total number of data points. This value represents the average squared deviation from the mean.

3. What does a high or low variance indicate about a distribution?

A high variance indicates that the data points are more spread out from the mean, while a low variance indicates that the data points are more clustered around the mean. In other words, a high variance suggests a greater diversity in the data, while a low variance suggests a more homogeneous distribution.

4. How is variance related to standard deviation?

Variance and standard deviation are closely related, as they both measure the spread of data in a distribution. The standard deviation is simply the square root of the variance, and it is often used as a more easily interpretable measure of variability in the data.

5. Can variance be negative?

No, variance cannot be negative. It is always a positive value, as it represents the average squared deviations from the mean. If you encounter a negative value when calculating variance, it is likely due to an error in the calculation or data entry.

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