Find the standard deviation of the values of ##y##

In summary, the conversation discusses finding the standard deviation of a variable, y, given a set of discrete variables, x, and their corresponding frequencies. The solution involves using the fact that the frequencies are the same and finding the ratio of a:b:c to determine the variance of y. The conversation also addresses a typo in one of the formulas and clarifies that the constant 1 does not affect the calculation of the variance.
  • #1
chwala
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Homework Statement
See attached- sent as received both question and solution.
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This is the question;

1676544903722.png
This is the solution as received;

1676544963497.png


I am not familiar with the approach used in the solution...my thinking was as follows

The frequencies are the same...the only thing changing are the discrete variables thus;

Let ##[x= 2,4,6]## and ##[y=7,13,19]## form a sequence...then the nth term is given by;

##x_n=2n## and ##y_n=(3⋅2n)+1=6n+1=3(x_n)+1##

##y_n=3(x_n)+1##

I will need to think on this...your input is welcome though as we already have the solution given...not unless it is wrong. Cheers.

I will look at this later...should be achievable! Since they've given us standard deviation for ##x## ... and both frequencies have same values then I would need to find the ratio of ##a:b:c## then use that to determine the standard deviation of ##y##.
 
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  • #2
chwala said:
The frequencies are the same...the only thing changing are the discrete variables thus;
Let ##[x= 2,4,6]## and ##[y=7,13,19]## form a sequence...
The part about "form a sequence" doesn't make sense to me. Really, what you're talking about is a map (i.e., function) from the x values to the y values.
chwala said:
Let ##[x= 2,4,6]## and ##[y=7,13,19]## form a sequence...then the nth term is given by;
##x_n=2n## and ##y_n=(3⋅2n)+1=6n+1=3(x_n)+1##
##y_n=3(x_n)+1##
The last line gives the function. That is, Y = 3X + 1.

There are several properties of the variance (see https://en.wikipedia.org/wiki/Variance, in the section titled Addition and multiplication by a constant.
Among them are
  • Var(X + a) = Var(X)
  • Var(aX) = a2Var(X)
From these, you should be able to work out the value of Var(Y) = Var(3X + 1), and use the given value of ##\sigma_X## to find ##\sigma_Y## AKA SD(Y). Note that Var(X) = ##(\sigma_X)^2##.
 
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  • #3
Mark44 said:
The part about "form a sequence" doesn't make sense to me. Really, what you're talking about is a map (i.e., function) from the x values to the y values.

The last line gives the function. That is, Y = 3X + 1.

There are several properties of the variance (see https://en.wikipedia.org/wiki/Variance, in the section titled Addition and multiplication by a constant.
Among them are×
  • Var(X + a) = Var(X)
  • Var(aX) = a2Var(X)
From these, you should be able to work out the value of Var(Y) = Var(3X + 1), and use the given value of ##\sigma_X## to find ##\sigma_Y## AKA SD(Y). Note that Var(X) = ##(\sigma_X)^2##.

That is fine and clear ...but what happens to the ##1##? all values are scaled by a constant...and i think ##1## is also a part of that constant. Should we not have;

##Var (Y)=\sqrt {a^2×Var(3x)+1}=2.82## to two decimal places?
 
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  • #4
Sorry, I made a typo (now corrected) in one of the formulas I wrote: It should be Var(X + a) = Var(X), not Var(a) as I originally wrote.

So if U = 3x, Var(U) = Var(3x), and so Var(U + 1) = Var(U).

chwala said:
Should we not have; ##Var (Y)=\sqrt {a^2×Var(3x)+1}=2.82## to two decimal places?
No, that's wrong on several counts.
##Var (Y) \ne \sqrt {a^2×Var(3x)+1}## -- you're mixing up variance and standard deviation.
Var(Y) = Var(3X) = 32 Var(X)
So SD(Y) = ##\sqrt{9 Var(X)} = 3 \sqrt{Var(X)}##

The answer given in the image you posted, 2.64, is correct. 2.82 is not.

chwala said:
but what happens to the 1?
It doesn't affect the calculation of the variance. A vertical translation in the data makes no difference. For example, the sets {1, 2, 3} and {3, 4, 5} have different means but the same variance, and hence the same standard deviations.
 
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FAQ: Find the standard deviation of the values of ##y##

What is the formula for calculating the standard deviation?

The formula for calculating the standard deviation (σ) of a set of values is: σ = sqrt(Σ(y - μ)² / N), where y represents each value, μ is the mean of the values, and N is the number of values.

How do you find the mean (μ) of the values?

To find the mean (μ) of the values, sum all the values together and then divide by the number of values. Mathematically, μ = Σy / N, where Σy is the sum of all values and N is the total number of values.

What is the difference between population standard deviation and sample standard deviation?

The population standard deviation is used when you have data for the entire population and is calculated with N in the denominator. The sample standard deviation is used when you have a sample of the population and is calculated with N-1 in the denominator to account for the sample size. The formula for sample standard deviation is: s = sqrt(Σ(y - μ)² / (N - 1)).

Why is standard deviation important in data analysis?

Standard deviation is important because it provides a measure of the dispersion or variability in a set of values. It indicates how spread out the values are around the mean, helping to understand the distribution and reliability of the data.

Can standard deviation be negative?

No, standard deviation cannot be negative because it is derived from the square root of the variance, which is always a non-negative value. Standard deviation represents a distance, and distances cannot be negative.

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