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Roy, did you ever get the code worked out to duplicate the Linear Regression
Slope function? (per below)
If so, could you share it?
Tom
----- Original Message -----
From: "Roy Larsen" <rlarsen@xxxxxxxxxxxxxx>
To: <equismetastock@xxxxxxxxxxxxxxx>
Sent: Saturday, August 14, 2004 9:57 PM
Subject: Re: [EquisMetaStock Group] Subject: Linear Regression / Linear
Regression Slope Relationship
> Hi Harry
>
> I'm totally lost with this mathematical jargon (which I admit is what I
asked for), but there a
> couple of lines of your post that make sense to me. They are...
>
> b=the slope (Linear Regression Slope)
>
> and
>
> b=[(x1-xbar)(y1-ybar)+.......+(xn-xbar)(yn-ybar)]/
> [(x1-xbar)sq +......+(xn-xbar)sq]
>
> Putting these two statements, can I assume that converting b correctly
into MetaStock code will give
> me an exact replica of the MFL Linear Regression Slope function?
>
> If that's the case then I am indebted to you.
>
> Regards
>
> Roy
>
> > On 14 Aug 2004 13:57:04 -0000, "Roy Larsen" <rlarsen@xxxxxxxxxxxxxx>
> > wrote:
> >
> > >>
> > >>Can anyone help with the mathematical relationship between Linear
Regression and Linear
> Regression
> > >>Slope.
> >
> > The formula for linear regression is:
> > y=a+bx+E
> >
> > a=the intercept
> > b=the slope (Linear Regression Slope)
> > E=the error
> >
> > normally you already have x and y. They are your paired data points.
> > You calculate b, then you estimate a (i.e., assume E=0).
> >
> > Since I don't have symbols on my E-mail program, my definitions of
> > terms are as follows:
> > n=number of pair values (x and y)
> > x1=first value of x
> > xn=nth value of x
> > xbar=sample mean of the x values
> > sq=term squared
> > sqrt=square root of term
> > *=multiply
> >
> > b=[(x1-xbar)(y1-ybar)+.......+(xn-xbar)(yn-ybar)]/
> > [(x1-xbar)sq +......+(xn-xbar)sq]
> >
> > a=(ybar)-(b)(xbar)
> >
> > You can calculate the linear regression with the trend () function in
> > Excel.
> >
> > My statistics book reminded me that the slope by itself cannot tell
> > you how strongly correlated x and y are. For that you use the Pearson
> > correlation. In Excel, it is Pearson().
> >
> > The formula for the Pearson correlation is:
> >
> > r=[(x1-xbar)(y1-ybar)+.......+(xn-xbar)(yn-ybar)]/
> > [{(x1-xbar)sq +......+(xn-xbar)sq}sqrt * {(y1-ybar)sq
> > +......+(yn-ybar)sq}sqrt]
> >
> > That is the easy part, Roy. I'll leave the MS coding to you!
> >
> > Harry
> >
> >
> >
> >
> >
> > Yahoo! Groups Links
> >
> >
> >
> >
> >
> >
>
>
>
>
>
>
> Yahoo! Groups Links
>
>
>
>
>
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