Help Solve Variance Question with 80 Students

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  • Thread starter Yankel
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In this context, using the population variance for both samples would give the same result (k = 80). However, if we were using sample variances, the formula would be slightly different and we would get k = 82.5. Hope this helps!
  • #1
Yankel
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Hello,

Any help with this one would be appreciated.

In a college 80 students are taking the exam in the spring semester. Their grades has a mean and standard deviation.
In the summer semester, k additional students are being tested. All k students get grades which are equal to the average grade of the 80 students from the spring semester. After combining both samples, we get a standard deviation which is half of the standard deviation of the first 80 students. Find k.

I did something, and got k=80, but I am not sure it's correct, would appreciate if anyone can validate my answer.

what I did was to take S1 (the standard deviation before any addition) and I said it is equal to 2*S2 (which is the standard deviation of the two samples together).

Then I realized, that since all observations in the second sample are equal to the mean, than the numerator is equal in both parts of the equations (apart from the 2 of course). Moreover, the sums of deviance are equal.

Then I solved the equation to get k=80

P.S In this context, when I say variance I mean the formula in which we divide by n, not by n-1.
 
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  • #2
Yankel said:
Hello,

Any help with this one would be appreciated.

In a college 80 students are taking the exam in the spring semester. Their grades has a mean and standard deviation.
In the summer semester, k additional students are being tested. All k students get grades which are equal to the average grade of the 80 students from the spring semester. After combining both samples, we get a standard deviation which is half of the standard deviation of the first 80 students. Find k.

I did something, and got k=80, but I am not sure it's correct, would appreciate if anyone can validate my answer.

what I did was to take S1 (the standard deviation before any addition) and I said it is equal to 2*S2 (which is the standard deviation of the two samples together).

Then I realized, that since all observations in the second sample are equal to the mean, than the numerator is equal in both parts of the equations (apart from the 2 of course). Moreover, the sums of deviance are equal.

Then I solved the equation to get k=80

P.S In this context, when I say variance I mean the formula in which we divide by n, not by n-1.

Hi Yankel! :)

What you have is pretty close!

The one oversight is that when you say that the numerator is equal, you are talking about the variance instead of the standard deviation.
In formula form:

$\sigma_1^2 = \dfrac{{SS}_1}{80}$

$\sigma_2^2 = \dfrac{{SS}_2}{80 + k} = \dfrac{{SS}_1 + k \cdot 0}{80 + k}$

where ${SS}_1$ is the sum of the squared deviations for the first sample, and ${SS}_2$ is the sum for the complete sample.

P.S.: A variance in which we divide by n is called a population variance, as opposed to a sample variance.
 

FAQ: Help Solve Variance Question with 80 Students

What is variance and why is it important in statistics?

Variance is a statistical measure of how spread out a data set is from its mean. It is important because it helps us understand the variability and distribution of data, and can be used to make inferences and predictions.

How do you calculate variance?

Variance is calculated by taking the sum of the squared differences between each data point and the mean, divided by the total number of data points.

How can variance be used to compare data sets?

Variance can be used to compare the spread or variability of data sets. A smaller variance indicates that the data points are closer to the mean, while a larger variance means the data points are more spread out.

What is the relationship between variance and standard deviation?

Variance and standard deviation are both measures of variability in a data set. Standard deviation is simply the square root of the variance, making it a more commonly used measure as it is easier to interpret.

How can you interpret the variance of a data set?

The size of the variance can give an idea of how much the data points deviate from the mean. A small variance indicates that the data is closely clustered around the mean, while a large variance suggests that the data is more spread out. However, it is important to also consider the mean and other measures of central tendency when interpreting variance.

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