Exploring Non-Linear Regression Methods for Data Analysis

In summary, the individual is seeking a method to produce a non-linear regression line for data that follows a non-linear pattern. They mention the options of using Newton's interpolating polynomial or Lagrange's polynomial, but also inquire about other methods such as power functions, exponential functions, or logarithmic functions. They mention that Excel has the functionality to produce these types of functions, but are unsure of how to do so. They are specifically looking for the expression of these functions in Excel, and mention the LOGEST and LINEST functions as potential options.
  • #1
Cygni
38
0

Homework Statement


Hi, the problem that I am struggling with is getting an appropriate method to produce non - linear line of regression when the data follows non - linear pattern.

Homework Equations



Would it be appropriate to use Newton's interpolating polynomial or Lagrange's polynomial at such circumstances? (one could select the most appropriate data points and then construct a polynomial using either method). Perhaps there are other methods that could be in this situation?
 
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  • #2
No one has responded, so I'll take a shot. There are lots of options to choose from after you decide a regression line won't do the job, and the option to use would depend on your data. Besides using power functions or polynomials (including the two you mentioned), other options are exponential functions or logarithmic functions. Many spreadsheets, such as Excel, have this functionality, and let you choose which type to use.
 
  • #3
Mark44 said:
No one has responded, so I'll take a shot. There are lots of options to choose from after you decide a regression line won't do the job, and the option to use would depend on your data. Besides using power functions or polynomials (including the two you mentioned), other options are exponential functions or logarithmic functions. Many spreadsheets, such as Excel, have this functionality, and let you choose which type to use.

Mark44, thank You for your response. I have came across the fact that excel can produce various functions for the data, however, I was unable to find the expression of such functions in the excel, and therefore I am unable to extrapolate the data. Perhaps there is something i do not know about excel?

Regards,
Cygni.
 
  • #4
There is the LOGEST function in Excel that can be used to fit your data to a curve of the form y = bmx. There is also LINEST, which you can use to fit your data to y = mx + b, a straight line. I thought I remembered Excel having other functions of this type, but I don't see them in my copy of Excel 2007.
 

Related to Exploring Non-Linear Regression Methods for Data Analysis

1. What are non-linear statistics?

Non-linear statistics is a branch of statistics that deals with non-linear relationships between variables. It involves the use of mathematical models and methods to analyze data that does not follow a linear pattern.

2. What are some examples of non-linear relationships?

Some examples of non-linear relationships include exponential relationships, logarithmic relationships, polynomial relationships, and power relationships. These relationships do not follow a straight line when graphed.

3. How is non-linear statistics different from linear statistics?

Linear statistics deals with relationships between variables that can be expressed as a straight line, while non-linear statistics deals with relationships that cannot be represented by a straight line. Non-linear statistics also uses different methods and models compared to linear statistics.

4. What are the advantages of using non-linear statistics?

Non-linear statistics allows for a more accurate representation of real-life data, as many relationships in nature and social sciences are non-linear. It also provides a better understanding of complex relationships between variables and can capture non-linear patterns that would be missed by linear methods.

5. What are some common techniques used in non-linear statistics?

Some common techniques used in non-linear statistics include regression analysis, curve fitting, non-parametric methods, and machine learning algorithms. These techniques help to model and analyze non-linear relationships between variables and make predictions based on the data.

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