[Blindmath] statistical formulas

[Jonathan Godfrey]

Hi Arielle and others,

Actually, Jaws can read some Greek, or has done. It totally depends on the
font used by the author and the version of jaws being used. It's just so
unreliable that I don't bother looking for it anymore.

Even if people are not taking  a lot of mathematics courses, I'd recommend
getting a handle on basic latex commands. It is possible to convert all
mathematical  content to latex which is readable by your screen readers. The
add-on software is called MathType and is a commercial product. Sorry I have
no idea of cost as it is all sorted for me by my employer. Many latex
commands are pretty obvious and the general structure should be understood
by anyone familiar with code like Nemith (for example).

Jonathan


 

-----Original Message-----
From: Blindmath [mailto:blindmath-bounces at nfbnet.org] On Behalf Of Arielle
Silverman
Sent: Wednesday, 24 October 2012 1:34 p.m.
To: Blind Math list for those interested in mathematics
Subject: Re: [Blindmath] statistical formulas

Thanks Jonathan. I wonder if anyone has worked on scripting JAWS or its
competitors to read Greek letters?
Arielle

On 10/23/12, Jonathan Godfrey <a.j.godfrey at massey.ac.nz> wrote:
> Hi all,
>
> I've decided to jump in here as I've spotted a small (but crucial) 
> error in the contributions given thus far. I'd also point out that the 
> lecturing staff at most universities now have the ability to put Greek 
> and formula into the text of email message in the same way they do in word
documents.
> They won't be readable either if done that way.
>
>
>
> Correlation is the covariance divided by the square root of the variances.
> For a population, the variance is
>
> Var(x) = Sum[(x-mu)^2]/n
>
> where n is the population size and mu is the population mean. Note 
> that sum[] means to sum over all observations.
>
> Expanding that out so that there is no squaring going on would give:
>
> Var(x) = Sum[(x-mu)(x-mu)]/n
>
> If you don't do the division by n then this is the sum of squares 
> sometimes shortened to SS, or to denote the variable x, S_xx
>
>
> A covariance is found using:
>
> Cov(x,y) = Sum[(x-mu_x)(y-mu_y)]/n
>
> Where the mu is relevant to either the x or y and therefore gets given 
> the subscripts.
>
>
>
> The reduction to alternate forms comes because the cross product S_xy 
> is the numerator of the covariance. This means we can write the 
> correlation as:
>
> Cor(x,y) = Cov(x,y)/sqrt[Var(x)Var(y)] Or
> Cor(x,y) = S_xy / sqrt[S_xx S_yy]
>
> Another notation uses the fact that the square root of the variance is 
> the standard deviation. This means that we see the correlation 
> expressed as the covariance divided by the product of the standard
deviations.
>
> Mathematically it's all the same. The expression using the cross 
> products (sum of squares) working is equally useful for samples and
populations.
> Remember that the division is by (n-1) for samples for both 
> covariances and variances.
>
>
> Hope this helps.
>
> Jonathan
>
>
>
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