By ear
On Tue, Aug 6, 2019 at 07:51 Bob Sneidar via use-livecode <
use-livecode@lists.runrev.com> wrote:
> You mean to say relative math is easy?
>
> Bob S
>
>
> > On Aug 5, 2019, at 14:57 , Stephen Barncard via use-livecode <
> use-livecode@lists.runrev.com> wrote:
> >
> > This is the kind of
You mean to say relative math is easy?
Bob S
> On Aug 5, 2019, at 14:57 , Stephen Barncard via use-livecode
> wrote:
>
> This is the kind of math I use every day, without knowing what I’m doing.
>
> On Mon, Aug 5, 2019 at 09:21 Mark Wieder via use-livecode <
>
This is the kind of math I use every day, without knowing what I’m doing.
On Mon, Aug 5, 2019 at 09:21 Mark Wieder via use-livecode <
use-livecode@lists.runrev.com> wrote:
> On 8/5/19 9:00 AM, Mark Wieder via use-livecode wrote:
> > have to weight the measured values to determine the maximum
On 8/5/19 9:00 AM, Mark Wieder via use-livecode wrote:
have to weight the measured values to determine the maximum (and the Q
as desired).
Urk. Now it's my turn to have misspoken.
The maximum is easy to measure.
But looking at the clustering of values to determine the Q of the
bandpass filter
On 8/5/19 1:48 AM, hh via use-livecode wrote:
[@Mark: A (weighted) mean is a location parameter, one number.]
Yes, exactly.
In sum, Dagobert wants to change the method on base of the raw
data or change the raw data such that the results are the wished
ones. (Honi soit qui mal y pense ...)
When computing limits for distribution categories given
frequencies the following may be useful:
A number q is a p%-quantile of a data set
If the percentage of data nums <= q is >= p%
and the percentage of data nums >= q is >= (100-p)%
For each percentage p there is an interval
[lowerV,upperV]
> I wrote:
> In order to find these limits simply sort the random data (a random
> sample drawn out of the raw data) and take the values that have
> approximately 30% or 80% of the values below them (no scaling needed
> for that). In statistical terms: Find the 30% and 80% quantiles.
Please
I wrote:
> In order to find these limits simply sort the random data (a random
> sample drawn out of the raw data) and take the values that have
> approximately 30% or 80% of the values below them (no scaling needed
> for that). In statistical terms: Find the 30% and 80% quantiles.
Sorry, read
[@Mark: A (weighted) mean is a location parameter, one number.]
Here the customer (say Dagobert Duck) wants to change/weight the
distribution of the data.
As Dar says, he could do a mapping from 0-800 to bins as
"bad, neutral, good" simply by setting limits for the bins.
For example 0-30 = bad,
, 2019 7:07 PM
To: dsc--- via use-livecode
Cc: Mark Wieder
Subject: Re: [OT] Weighted distribution of Numbers
On 8/4/19 3:00 PM, dsc--- via use-livecode wrote:
> I'm unsure how often 800 or so changes. I'll call it 800, it is just a
name. Values can range from 0 through 800.
>
> You can map
On 8/4/19 3:00 PM, dsc--- via use-livecode wrote:
I'm unsure how often 800 or so changes. I'll call it 800, it is just a name.
Values can range from 0 through 800.
You can map a number in that range to 0-1 by dividing by 800. That is,
scaled1(n) is n/800.
I guess you want to map each number
ergreeninfo.net
>>
>>
>> -Original Message-
>> From: use-livecode [mailto:use-livecode-boun...@lists.runrev.com] On Behalf
>> Of Dar Scott Consulting via use-livecode
>> Sent: Sunday, August 04, 2019 4:33 PM
>> To: How to use LiveCode
>> Cc: Da
ces
> rdim...@evergreeninfo.net
>
>
> -Original Message-
> From: use-livecode [mailto:use-livecode-boun...@lists.runrev.com] On Behalf
> Of Dar Scott Consulting via use-livecode
> Sent: Sunday, August 04, 2019 4:33 PM
> To: How to use LiveCode
> Cc: Dar Scott Con
hat automated.
Ralph DiMola
IT Director
Evergreen Information Services
rdim...@evergreeninfo.net
-Original Message-
From: use-livecode [mailto:use-livecode-boun...@lists.runrev.com] On Behalf
Of Dar Scott Consulting via use-livecode
Sent: Sunday, August 04, 2019 4:33 PM
To: How to use LiveCode
C
I was thinking the same, but was to afraid to say it. Yes, the actual name is
"lying".
However, there might be an honest attempt to display crowded dots or icons.
> On Aug 4, 2019, at 2:19 PM, hh via use-livecode
> wrote:
>
>> Ralph D. wrote:
>> I'm sure there's an actual name for doing this
518-636-3998 Ex:11
> Cell: 518-796-9332
>
>
> -Original Message-
> From: use-livecode [mailto:use-livecode-boun...@lists.runrev.com] On Behalf
> Of Dar Scott Consulting via use-livecode
> Sent: Sunday, August 04, 2019 3:03 PM
> To: How to use LiveCode
> Cc
> Ralph D. wrote:
> I'm sure there's an actual name for doing this in the statistician's
> world but I don't know what it is.
This has nothing to do with "statistics".
This is simply "try to lie by data cheating".
___
use-livecode mailing list
Cell: 518-796-9332
-Original Message-
From: use-livecode [mailto:use-livecode-boun...@lists.runrev.com] On Behalf
Of Dar Scott Consulting via use-livecode
Sent: Sunday, August 04, 2019 3:03 PM
To: How to use LiveCode
Cc: Dar Scott Consulting
Subject: Re: [OT] Weighted distribution
On 8/4/19 11:49 AM, Ralph DiMola via use-livecode wrote:
I have a set of raw numbers(6,000 of them from 0 to 800 or so). It was easy
to normalize these numbers from 0 to 100. But as I look at the results I see
that there is one at to top(100) and a few in the 90s and many more in the
70s and
Just to clarify... Is this right?
The max of the raw numbers maps to 100.
The min of the raw numbers maps to 0. (Or is it 0 maps to 0?)
The middle number maps to something like 70. (Or is it half of the max maps to
70?)
The mapping is smooth.
Where 70 might be something else.
> On Aug 4, 2019,
I have a set of raw numbers(6,000 of them from 0 to 800 or so). It was easy
to normalize these numbers from 0 to 100. But as I look at the results I see
that there is one at to top(100) and a few in the 90s and many more in the
70s and 80s. I need to make these numbers more evenly distributed and
21 matches
Mail list logo
