What functions are best for approximating the Least Square Method?

In summary, the Least Square Method is a statistical technique used to find the best fit line or curve for a set of data points. It works by minimizing the sum of squared errors between the data points and the predicted values on the line or curve. The assumptions of this method include linearity, normal distribution of errors, constant variance and independence of errors. It is commonly used in various fields such as economics, finance, engineering and social sciences for predicting trends, analyzing the impact of factors and making informed decisions. However, it may not be suitable for non-linear relationships and can be sensitive to outliers. It also assumes no measurement errors and no multicollinearity among the independent variables.
  • #1
aldrinkleys
15
0
SKVuM.jpg


Which families of functions should I use to approximate it?

I tried f(x) = a + bx + cx^2 + dx^3 + ex^4 + fx^5 + gx^6

a,b,c,d,e,f,g [tex]\in[/tex] R

And I got this:

eIOaD.jpg


but that curve ignores the hole between 50 and 80.
and this is important for me and f(x) is too long...



Can anyone help me?
 
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  • #2
That fit is probably optimal if you want to fit all the data at the same time. You could break the data into two fits for better accuracy, however.
 
  • #3
ah, I solved my problem =)I used Harmonic analysis

bgolb.jpg
 

FAQ: What functions are best for approximating the Least Square Method?

What is the Least Square Method?

The Least Square Method is a statistical technique used to find the best fit line or curve for a set of data points. It is commonly used in regression analysis to determine the relationship between two or more variables.

How does the Least Square Method work?

The Least Square Method works by minimizing the sum of the squared errors between the data points and the predicted values on the line or curve. This is achieved by calculating the distance between each data point and the line, squaring it, and then adding all of the squared distances together. The line or curve with the lowest sum of squared errors is considered the best fit.

What are the assumptions of the Least Square Method?

The assumptions of the Least Square Method include the following: 1) the relationship between the variables is linear, 2) the errors in the data are normally distributed, 3) the errors have constant variance, and 4) the errors are independent of each other.

How is the Least Square Method used in real-world applications?

The Least Square Method is used in a variety of fields, including economics, finance, engineering, and social sciences. It can be used to predict future trends, analyze the impact of different factors on a variable, and make informed decisions based on data analysis.

What are the limitations of the Least Square Method?

The Least Square Method may not be suitable for non-linear relationships between variables, and it may be sensitive to outliers in the data. Additionally, it assumes that the independent variables are measured without error and that there is no multicollinearity among the independent variables.

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