Thursday, August 03, 2006

Wrought Iron Fence

Marie-Luise Schramm :

need help with statistics classDESPERATE!!!!!!!!!

Helen FitzGerald :

Not if u sneak out at 2:00am.

Wimie Wilhelm :

sorry this is in Italian, but it's the best explained I have found.

Danielle Summer :

http://brazil.mat.uniroma1.it/dario/biotec2003/statistica/node7.html

Yan Nam :

When you have made an idea on the concept, you can always take some data tables and look for the standard deviation using an Excel spreadsheet which has this and other statistical functions on it: it will help you getting familiar on how it works.

Michelle Meyrink :

Well, the standard deviation is a measure of dispersion, that is to say, a measure of how much the data in your sample are different from the mean. You're assuming that the data have a normal distribution (remember the Gauss bell-shaped curve). Well, with the SD you're comparing the shape of your curve with that model. A "tall and thin" curve indicates data that are very clustered about the mean; a "flat broad" curve means that the data are scattered and probably the mean is less informative than in the first case (you're stretching the confidence limits).

Lee Garlington :

You can find the formula and much more in the nice site at the link (I don't think I could manage math notation here).

Anabelle Lachatte :

There are LOTS of good web sites about standard deviations. However, maybe I can make it easier to read them.

Grace Renat :

Suppose you take a population, like all the boys in your school, and measure their heights. Some will be tall, and some will be short. Not all will be the same height. If you add up the number of boys that are between

Drina Pavlovic :

a. 4 feet and 4.5 feet

Ivory Stone :

b. 4.5 feet and 5 feet

Elise Muller :

c. 5 feet and 5.5 feet

Angie Ojciec :

d. 5.5 feet and 6 feet

Marcelle Larice :

e. 6 feet and 6.5 feet

Terri Firmer :

you can then plot these numbers. This plot is called a histogram. If you make the divisions a, b, c, d, e smaller, so you have more numbers to plot, then the histogram will be smoother.

Greta Scacchi :

The histogram will probably be shaped kind of like a bell, with very few really tall boys and really short boys. Most boys will have a height in the middle.

Jenny Agutter :

The MEAN is a measure of the the middle of this histogram. The STANDARD DEVIATION is a measure of how wide this histogram is; that is, how many boys will be very short or very tall, and not just a medium height. The STANDARD DEVIATION is said to describe the "spread" of the distribution. The STANDARD DEVIATION tells something about the chance that a boy you pick at random will be of medium height.

Asia Argento :

The STANDARD DEVIATION has a funny formula that is a bit confusing at first, but easy to use really.

Stella Carnacina :

Lets suppose that the height of the boys are described by the numbers

Isabel Florido :

{H1, H2, H3, ...Hn}

Lisa McCune :

i.e., you have the heights of n boys.

Laila Goody :

The first thing you do is find the arithmetic average of the boy's heights:

Jessica Brytn Flannery :

Hav=(1/n)*(H1+H2+...Hn)

Marielle De Palma :

Then, you find the squared distance of each height from the average height, Hav:

Paula Jacobs :

(H1-Hav)^2

Nikki Fairchild :

(H2-Hav)^2

Susanne Wuest :

(H3-Hav)^2

Diane Arcand :

and so on until

Dinara Drukarova :

(Hn-Hav)^2

Sally Phillips :

These squared differences are then added, and divided by n-1 (not n : the reason for this is technical and you can learn it later). Let

Christine Boisson :

V2=

Rocio Durcal :

[ (H1-Hav)^2 + (H2-Hav)^2 + ... + (Hn-Hav)^2 ] / (n-1)

Lynn Harris :

The number V2 is called the VARIANCE.

Jillian Kesner :

The square root of the variance is the STANDARD DEVIATION:

Luise Helm :

SD=(V2)^0.5

Anicee Alvina :

Let me see if I can answer your question in English. :-)

Keiko Aikawa :

I'm sure you have an equation in your notes or book that you can just start plugging stuff into. It probably looks like this:

Roxanne Kernohan :

s = square root of (X - X bar)^2/(n-1)

Buffy Tyler :

But if you want to know why it works, keep reading.

Ana Gabriel :

You have a list of numbers. First, you need to find the average, or mean, of those numbers, by adding them up and dividing by how many there were.

Karin Hofmann :

Now, if you subtract each number from the mean, you get a new list of numbers. These are the deviation scores, and tell you how far above or below average each number was. If you add them up, you will get zero, because some of them are positive and some negative. That's not very interesting, so we square them to make them all positive. Then we add up all those squared deviation scores.

Dragana Mrkic :

You probably got a pretty large number, and not the one we want, yet. We want the Standard Deviation, which is sort of like the average deviation. So just like you divide by the number of scores to get the average, you need to divide the sum of the squared deviation scores by something. Sometimes we divide it by the number of scores, like you would think, but for complicated reasons it works better if you divide by one less than that. So if you started with 5 numbers, divide by 4 now. (Be sure to check your notes or your book to see what your teacher said you should divide by.)

Letitia Farrell :

We're still one step away. The number you should have now is called the Variance. It is also a measure of how spread out your numbers were, but it's bigger than we want. Remember how we squared the deviation scores earlier? Now we need to undo that by taking the square root. What you get is the Standard Deviation you wanted.




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