Statistics: Standard Deviation Prob.

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Homework Help Overview

The discussion revolves around calculating the standard deviation from a given set of data points. The original poster presents a list of numerical values and seeks guidance on how to compute the standard deviation manually, rather than using statistical software.

Discussion Character

  • Exploratory, Conceptual clarification, Mathematical reasoning

Approaches and Questions Raised

  • Participants discuss the correct formula for standard deviation and variance, with one participant clarifying the components of the formula. There is an exploration of the original poster's misunderstanding regarding the equation and its application.

Discussion Status

The conversation has progressed with some participants providing clarifications and examples to aid understanding. The original poster acknowledges a mistake in their equation and expresses improved clarity on the topic. However, there is no explicit consensus on the best approach to manually calculate the standard deviation.

Contextual Notes

Participants note the distinction between variance and standard deviation, emphasizing that the square root must be taken to find the standard deviation. There are also references to alternative methods for calculation that may simplify the process.

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Homework Statement


This is my data: 42.4, 65.7, 29.8, 58.7, 52.1, 55.8, 57.0, 68.7, 67.3, 67.3, 54.3, 54.0 I need to find the standard deviation of this list of data.
2. Homework Equations standard deviation= : s^2=\frac{\sum_{i=1}^n(x_i -\overline{x})}{n-1} Actually the standard deviation is the square root of this expression.

3. I can use a statistics package like minitab or excell to solve this but how would I do it my hand? where would I start?[/b]
 
Last edited:
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Your formula is not correct, is that what messed you up? It should be:

s^2=\frac{\sum_{i=1}^n(x_i -\overline{x})^2}{n-1}

If that was not the problem then is there something you don't know how to do with the formula? Do you know what x_i and \overline{x} are?

x_i represents the data (for example x_1 is 42.4 and x_4 is 58.8)

And \overline{x} is the mean = \frac{x_1+x_2+...+x_n}{n}

Here is an example.

My data are the following: 14.3, 17.5, 15.6

So the mean, \overline{x} = \frac{14.3+17.5+15.6}{3} = 15.8

So then

(x_1 -\overline{x})^2 = (14.3 - 15.8)^2 = (-1.5)^2 = 2.25
(x_1 -\overline{x})^2 = (17.5 - 15.8)^2 = (1.7)^2 = 2.89
(x_1 -\overline{x})^2 = (15.6 - 15.8)^2 = (-.2)^2 = .04

Hence,

s^2 = \frac{2.25 + 2.89 + .04}{3-1} = \frac{5.18}{2} = 2.59

So our standard deviation s is then the square root of that which means s = 1.60935
 
Last edited:
Thanks, I did latex my equation wrong, but you also cleared up my confusion about how the expression works. :) I see my mistake in understanding now.
 
The equation is also for variance. If you want to find the actual standard deviation, you'll still need to take the square root of that.
 
By the way since
\Sigma_{i=1}^n (x_i- \overline{x})^2= \Sigma_{i=1}^nx^2- \overline{x}\Sigma_{i=1}^n x_i+ \overline{x}^2\Sigma_{i=1}^n 1
= \Sigma_{i=1}^n x_i^2- 2n\overline{x}+ n\overline{x}= \Sigma_{i=1}^n x_i^2- n\overlne{x}
(because
\Sigma_{i=1}^n x_i= n\overline{x}
and
\Sigma_{i=1}^n 1= n

\sigma^2= \frac{1}{n-1}\Sigma_{i=1}^n x_i^2- \frac{n}{n-1}\overline{x}

That's often easier to calculate.
 

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