FAQFAQ   SearchSearch   MemberlistMemberlist   UsergroupsUsergroups 
 ProfileProfile   PreferencesPreferences   Log in to check your private messagesLog in to check your private messages   Log inLog in 
Forum index » Science and Technology » Math » num-analysis
starting question: analysing the interations of historical data for prediction
Post new topic   Reply to topic Page 1 of 1 [2 Posts] View previous topic :: View next topic
Author Message
richardchaven
science forum beginner


Joined: 09 Jul 2006
Posts: 1

PostPosted: Sun Jul 09, 2006 4:24 pm    Post subject: starting question: analysing the interations of historical data for prediction Reply with quote

I am writing an application to allow users to record their blood
glucose levels, insulin injections (type and amount), meals, and
exercise. Once they enter a few weeks worth, I want to programatically
find the relationship between these numbers so I can predict how much
insulin they should take in order to end up with a desirable blood
level (e.g. 100 mg.dl) at any point in time.

I see about eight variables interacting with each other.

I took basic (business) staticstics in college, but have little
mathamatics experience other than that.

1. What is the thing I'm doing called ?

2. Where do I find out more about it ?

3. Is this likely to be something I have to learn and build, or doI
find/buy/impliment a standard library and push numbers into it ?

Thanks for the help.
Back to top
Peter Spellucci
science forum Guru


Joined: 29 Apr 2005
Posts: 702

PostPosted: Mon Jul 10, 2006 4:27 pm    Post subject: Re: starting question: analysing the interations of historical data for prediction Reply with quote

In article <1152462245.233629.262000@75g2000cwc.googlegroups.com>,
"richardchaven" <google@thistooshallpass.org> writes:
Quote:
I am writing an application to allow users to record their blood
glucose levels, insulin injections (type and amount), meals, and
exercise. Once they enter a few weeks worth, I want to programatically
find the relationship between these numbers so I can predict how much
insulin they should take in order to end up with a desirable blood
level (e.g. 100 mg.dl) at any point in time.

I see about eight variables interacting with each other.

I took basic (business) staticstics in college, but have little
mathamatics experience other than that.

1. What is the thing I'm doing called ?

2. Where do I find out more about it ?

3. Is this likely to be something I have to learn and build, or doI
find/buy/impliment a standard library and push numbers into it ?

Thanks for the help.


time series analysis.
for example "Quantitative Forecasting Methods by Farnum & Stanton"

don't reenvent the wheel:

for example:

P. PALUMBO, W.H. ONG-CLAUSEN, S. PANUNZI and A. DE GAETANO
Linear periodic models of subcutaneous insulin absorption:
mathematical analysis

Journal: HERMIS- An International Journal of
Computer Mathematics and its Applications
ISSN: 1108-7609
Volume: 7 - Special Issue
Date: June 2006


hth
peter
Back to top
Google

Back to top
Display posts from previous:   
Post new topic   Reply to topic Page 1 of 1 [2 Posts] View previous topic :: View next topic
The time now is Sat Nov 17, 2018 11:17 pm | All times are GMT
Forum index » Science and Technology » Math » num-analysis
Jump to:  

Similar Topics
Topic Author Forum Replies Last Post
No new posts Question about Life. socratus Probability 0 Sun Jan 06, 2008 10:01 pm
No new posts Probability Question dumont Probability 0 Mon Oct 23, 2006 3:38 pm
No new posts Question about exponention WingDragon@gmail.com Math 2 Fri Jul 21, 2006 8:13 am
No new posts question on solartron 1260 carrie_yao@hotmail.com Electrochem 0 Fri Jul 21, 2006 7:11 am
No new posts A Combinatorics/Graph Theory Question mathlover Undergraduate 1 Wed Jul 19, 2006 11:30 pm

Copyright © 2004-2005 DeniX Solutions SRL
Other DeniX Solutions sites: Electronics forum |  Medicine forum |  Unix/Linux blog |  Unix/Linux documentation |  Unix/Linux forums  |  send newsletters
 


Powered by phpBB © 2001, 2005 phpBB Group
[ Time: 0.0144s ][ Queries: 16 (0.0042s) ][ GZIP on - Debug on ]