EGR 103/Fall 2016/Lab 8

The following document is meant as an outline of what is covered in this assignment.

Typographical Errors

None yet!

Specific Problems

Palm 6.10

• Part of the goal here is to see that while, mathematically, "more is better," having a higher order may not make much of an improvement. The key here is to figure out the "just right" order where adding terms make important changes in how the model predicts the values.
• You can use polyfit and polyval to get the coefficients and estimates.
• Don't forget to have the \(S_t\) value somewhere!

Palm 6.16

• This problem is a straight-forward general linear regression.
• If you want, you can use the General Linear Regression Example Code or you can start from scratch. If you use the example, be sure to use the appropriate version of the honor code.

Chapra 15.12

• This problem requires a general linear fit method to solve for the coefficients.
• Be sure to include the statistical information and the estimates in the lab report.

Chapra 14.12

• This problem uses a linearized version of power law to solve for the coefficients.
• If you want, you can use the Linearized Regression Example Code or you can start from scratch. If you use the example, be sure to use the appropriate version of the honor code.
• You do not need to include the extra code for the other models if you want to make the program shorter.

Chapra 14.14

• This problem uses a linearized version of the saturation growth model to solve for the coefficients.
• If you want, you can use the Linearized Regression Example Code or you can start from scratch. If you use the example, be sure to use the appropriate version of the honor code.
• You do not need to include the extra code for the other models if you want to make the program shorter.

Chapra 15.7

• This is a multidimensional general linear fit.
• If you want, you can use the General Multidimensional Linear Regression Example Code or you can start from scratch. If you use the example, be sure to use the appropriate version of the honor code.
• Don't forget to calculate the required estimate and error in that estimate!

Chapra 15.10 Alternate

• These are both general linear regressions.
• If you want, you can use the General Linear Regression Example Code or you can start from scratch. If you use the example, be sure to use the appropriate version of the honor code.
• Be sure you create two graphs for each piece - one for the total population and one with the three populations shown separately.
• Also be sure to answer all the questions.

Chapra 14.11 (Linearized)

• If you want, you can use the Linearized Regression Example Code or you can start from scratch. If you use the example, be sure to use the appropriate version of the honor code.
• Plan out your variable names before starting - there are several things to keep track of for this problem!

Chapra 14.11 (Nonlinear Regression)

• If you want, you can use the Nonlinear Regression Example Code or you can start from scratch. If you use the example, be sure to use the appropriate version of the honor code.
• Plan out your variable names before starting - there are several things to keep track of for this problem!
• Be sure to include your discussion.