Difference: DataFitting (1 vs. 10)

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FORM FIELD Conclusions Conclusions In conclusion...
FORM FIELD ExtraForExperts ExtraForExperts
|*FORM FIELD StudentTasks*|StudentTasks|
  1. Write AMPL files regression.mod and regression.run that use regression.dat to solve The Data Fitting Problem. Write a management summary of your solution. Be sure to indicate which of the functions fits the data best.

    What to hand in Your new AMPL files regression.mod and regression.run. Your management summary.

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Revision 92008-04-02 - MichaelOSullivan

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Revision 62008-03-01 - TWikiAdminUser

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Operations Research Topics: NonlinearProgramming

Application Areas: Data Analysis

Contents

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Problem Description

Also known as The Least Squares Problem

When modelling data, scientists often want to fit an analytical model to their experimental data. In this case an experiment has provided some $(x, y)$-coordinates that have a growth curve shape, i.e., initially $y$ increases quickly with $x$, thens tails off to some maximum value. The scientists in this study have suggested three possible functions to model this behaviour:

  1. $y = f(x) \equiv \displaystyle\frac{1}{a x^2 + b x + c}$
  2. $y = f(x) \equiv a\log(x - b) + c$
  3. $y = f(x) \equiv a\sqrt{x - b} + c$

Given one of the possible functions $f(x)$ and the data points $(x_i, y_i), i=1, \ldots, n$ they want to find $a$, $b$ and $c$ to minimize the squared distance between the data points and the estimates, i.e.,

\begin{equation*} \min_{a, b, c} \sum_{i=1}^n \left(y_i - f(x_i) \right)^2 \end{equation*}

The scientists have specified the number of data points and their coordinates in an AMPL data file regression.dat.

The scientists want to know which of the suggested functions provides the best fit to the data.

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Problem Formulation

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Computational Model

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Results

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Conclusions

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  1. Write AMPL files regression.mod and regression.run that use regression.dat to solve The Data Fitting Problem. Write a management summary of your solution. Be sure to indicate which of the functions fits the data best.

    
What to hand in  Your new AMPL files regression.mod and regression.run. Your management summary.


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Revision 52008-03-01 - TWikiAdminUser

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Operations Research Topics: NonlinearProgramming

Application Areas: Data Analysis

Contents

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Problem Description

Also known as The Least Squares Problem

When modelling data, scientists often want to fit an analytical model to their experimental data. In this case an experiment has provided some $(x, y)$-coordinates that have a growth curve shape, i.e., initially $y$ increases quickly with $x$, thens tails off to some maximum value. The scientists in this study have suggested three possible functions to model this behaviour:

  1. $y = f(x) \equiv \displaystyle\frac{1}{a x^2 + b x + c}$
  2. $y = f(x) \equiv a\log(x - b) + c$
  3. $y = f(x) \equiv a\sqrt{x - b} + c$

Given one of the possible functions $f(x)$ and the data points $(x_i, y_i), i=1, \ldots, n$ they want to find $a$, $b$ and $c$ to minimize the squared distance between the data points and the estimates, i.e.,

\begin{equation*} \min_{a, b, c} \sum_{i=1}^n \left(y_i - f(x_i) \right)^2 \end{equation*}

The scientists have specified the number of data points and their coordinates in an AMPL data file regression.dat.

The scientists want to know which of the suggested functions provides the best fit to the data.

Return to top

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Problem Formulation

 
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Computational Model

 
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Results

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Conclusions

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Conclusions

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  1. Write AMPL files regression.mod and regression.run that use regression.dat to solve The Data Fitting Problem. Write a management summary of your solution. Be sure to indicate which of the functions fits the data best.

    What to hand in Your new AMPL files regression.mod and regression.run. Your management summary.

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META FORM name="OpsRes.CaseStudyForm"
FORM FIELD Title Title DataFitting
FORM FIELD DateSubmitted DateSubmitted 19 Feb 2008
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FORM FIELD CaseStudyType CaseStudyType DIYCaseStudy
 
FORM FIELD OperationsResearchTopics OperationsResearchTopics NonlinearProgramming
FORM FIELD ApplicationAreas ApplicationAreas Data Analysis
|*FORM FIELD ProblemDescription*|ProblemDescription|*THE DATA FITTING PROBLEM*

Also known as The Least Squares Problem

When modelling data, scientists often want to fit an analytical model to their experimental data. In this case an experiment has provided some $(x, y)$-coordinates that have a growth curve shape, i.e., initially $y$ increases quickly with $x$, thens tails off to some maximum value. The scientists in this study have suggested three possible functions to model this behaviour:

  1. $y = f(x) \equiv \displaystyle\frac{1}{a x^2 + b x + c}$
  2. $y = f(x) \equiv a\log(x - b) + c$
  3. $y = f(x) \equiv a\sqrt{x - b} + c$

Given one of the possible functions $f(x)$ and the data points $(x_i, y_i), i=1, \ldots, n$ they want to find $a$, $b$ and $c$ to minimize the squared distance between the data points and the estimates, i.e.,

\begin{equation*} \min_{a, b, c} \sum_{i=1}^n \left(y_i - f(x_i) \right)^2 \end{equation*}

The scientists have specified the number of data points and their coordinates in an AMPL ??? LINK data file =regression.dat=.

The scientists want to know which of the suggested functions provides the best fit to the data.|

Revision 42008-02-23 - MichaelOSullivan

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Revision 32008-02-19 - LaurenJackson

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Revision 22008-02-19 - LaurenJackson

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Revision 12008-02-19 - LaurenJackson

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Case Study: DataFitting

Submitted: 19 Feb 2008

Operations Research Topics: NonlinearProgramming

Application Areas: Data Analysis

Contents

Problem Description

Also known as The Least Squares Problem

When modelling data, scientists often want to fit an analytical model to their experimental data. In this case an experiment has provided some $(x, y)$-coordinates that have a growth curve shape, i.e., initially $y$ increases quickly with $x$, thens tails off to some maximum value. The scientists in this study have suggested three possible functions to model this behaviour:

  1. $y = f(x) \equiv \displaystyle\frac{1}{a x^2 + b x + c}$
  2. $y = f(x) \equiv a\log(x - b) + c$
  3. $y = f(x) \equiv a\sqrt{x - b} + c$

Given one of the possible functions $f(x)$ and the data points $(x_i, y_i), i=1, \ldots, n$ they want to find $a$, $b$ and $c$ to minimize the squared distance between the data points and the estimates, i.e.,

\begin{equation*} \min_{a, b, c} \sum_{i=1}^n \left(y_i - f(x_i) \right)^2 \end{equation*}

The scientists have specified the number of data points and their coordinates in an AMPL data file regression.dat.

The scientists want to know which of the suggested functions provides the best fit to the data.

Return to top

Problem Formulation

The formulation...

Return to top

Computational Model

The computational model...

Return to top

Results

The results...

Return to top

Conclusions

In conclusion...

Return to top

Student Tasks

  1. Write AMPL files regression.mod and regression.run that use regression.dat to solve The Data Fitting Problem. Write a management summary of your solution. Be sure to indicate which of the functions fits the data best.

    What to hand in Your new AMPL files regression.mod and regression.run. Your management summary.

Return to top

META FORM name="OpsRes.CaseStudyForm"
FORM FIELD Title Title DataFitting
FORM FIELD DateSubmitted DateSubmitted 19 Feb 2008
FORM FIELD OperationsResearchTopics OperationsResearchTopics NonlinearProgramming
FORM FIELD ApplicationAreas ApplicationAreas Data Analysis
|*FORM FIELD ProblemDescription*|ProblemDescription|*THE DATA FITTING PROBLEM*

Also known as The Least Squares Problem

When modelling data, scientists often want to fit an analytical model to their experimental data. In this case an experiment has provided some $(x, y)$-coordinates that have a growth curve shape, i.e., initially $y$ increases quickly with $x$, thens tails off to some maximum value. The scientists in this study have suggested three possible functions to model this behaviour:

  1. $y = f(x) \equiv \displaystyle\frac{1}{a x^2 + b x + c}$
  2. $y = f(x) \equiv a\log(x - b) + c$
  3. $y = f(x) \equiv a\sqrt{x - b} + c$

Given one of the possible functions $f(x)$ and the data points $(x_i, y_i), i=1, \ldots, n$ they want to find $a$, $b$ and $c$ to minimize the squared distance between the data points and the estimates, i.e.,

\begin{equation*} \min_{a, b, c} \sum_{i=1}^n \left(y_i - f(x_i) \right)^2 \end{equation*}

The scientists have specified the number of data points and their coordinates in an AMPL ??? LINK data file =regression.dat=.

The scientists want to know which of the suggested functions provides the best fit to the data.|

FORM FIELD ProblemFormulation ProblemFormulation The formulation...
FORM FIELD ComputationalModel ComputationalModel The computational model...
FORM FIELD Results Results The results...
FORM FIELD Conclusions Conclusions In conclusion...
FORM FIELD ExtraForExperts ExtraForExperts
|*FORM FIELD StudentTasks*|StudentTasks| 1 Write AMPL files regression.mod and regression.run that use regression.dat to solve The Data Fitting Problem. Write a management summary of your solution. Be sure to indicate which of the functions fits the data best.

What to hand in Your new AMPL files regression.mod and regression.run. Your management summary. |

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