How to find error between non-linear plot and data points?

Thread starter cantRemember
Start date Feb 21, 2013
Tags

Data Error Non-linear Plot Points

In summary, to determine the error between a non-linear plot and data points, you can use least squares regression to find the line of best fit and measure the distance between points and the line. Linear regression involves fitting a straight line while non-linear regression involves fitting a curve. The choice between linear and non-linear plots depends on the trend of the data points. Common sources of error in non-linear plots include measurement and data entry errors as well as outliers, which should be removed before fitting the plot. To improve accuracy, you can increase the number of data points, remove outliers, and use a more sophisticated regression method. Properly labeling axes and communicating assumptions and limitations is also important.

Feb 21, 2013

cantRemember

Is there a formal way to measure the error between some arbitrary points and a non-linear curve in order to minimize it?

Mathematics news on Phys.org

Feb 21, 2013

Simon Bridge

Science Advisor

Homework Helper

The short answer is "yes".
You can regress to a curve in many ways... least-squares is popular.
What's best depends on the curve and what you want to know.
I'm guessing you already know how to regress to a line?

Related to How to find error between non-linear plot and data points?

1. How do I determine the error between a non-linear plot and data points?

To determine the error between a non-linear plot and data points, you can use a method called least squares regression. This involves finding the line of best fit for the data points and measuring the distance between the points and the line. The sum of these distances is the error between the plot and data points.

2. What is the difference between linear and non-linear regression?

Linear regression involves fitting a straight line to a set of data points, while non-linear regression involves fitting a curve (such as a quadratic or exponential) to the data points. Non-linear regression is used when the relationship between the variables is not linear.

3. How do I know if my data points are better represented by a linear or non-linear plot?

This can be determined by visually inspecting the data points and looking for any noticeable patterns or trends. If the points appear to follow a straight line, then a linear plot would be more appropriate. However, if the points seem to follow a curve, then a non-linear plot would be a better fit.

4. What are some common sources of error in non-linear plots?

Some common sources of error in non-linear plots include measurement error, data entry errors, and outliers. It is important to carefully review the data and remove any outliers before fitting a non-linear plot.

5. How can I improve the accuracy of my non-linear plot?

To improve the accuracy of a non-linear plot, you can increase the number of data points, carefully review and remove any outliers, and use a more sophisticated regression method such as weighted least squares. It is also important to properly label the axes and clearly communicate any assumptions or limitations of the plot.