Data Fitting

버전 3.0 (167 KB) 작성자: Dr. NEETU PRASAD
This app does the regression of data given by user for polynomial, exponential, power, fourier, linear multiple regression functions.
다운로드 수: 3.4K
업데이트 날짜: 2022/8/28

라이선스 보기

This app provides a platform to import the data, visualize it and construct a curve or mathematical function, that has the best fit to the series of data points.
DESCRIPTION
Data fitting is the process of fitting models to data and analysing the accuracy of the fit. Engineers and scientists use data fitting techniques, including mathematical equations and nonparametric methods, to model acquired data.
This app fits the data in graphical form and also gives the fitting parameters that describe the mathematical relation between the given data variables and the quality of the fit.
Data fitting is the way we model or represent a data spread by assigning a 'best fit' function (curve) along with the entire range. Ideally, it will capture the trend in the data and allow us to make predictions of how the data series will behave in the future which includes:
(1) POLYNOMIAL
(2) EXPONENTIAL
(3) POWER
(4)FOURIER
(5)MULTIPLE LINEAR REGRESSION
And it also includes the nonparametric function "SMOOTHING" which smoothens the given data.
AUTHORS:
SAGAR
MD. MOHZZAM
FRANSI MITTAL
NEETU PRASAD

인용 양식

NEETU PRASAD, SAGAR, MD. MOHZZAM, FRANSI MITTAL (2022). Data Fitting (https://www.mathworks.com/matlabcentral/fileexchange/110730), MATLAB Central File Exchange. Retrieved August 28, 2022

MATLAB 릴리스 호환 정보
개발 환경: R2022a
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버전 게시됨 릴리스 정보
3.0

UPDATED FEATURES:
1. Access to MATLAB Workspace for importing data
2. Export function to save data plot and table
3. Log axis button to convert x and y axis into log axis
4. New layout
5. Smoothing using moving average
6. Reset button

2.0

Updated features:
1. Access to MATLAB workspace for importing data
2. Export function to save figures and data table after regression
3. Log axis button converts x and y axis into log scale
4. Reset button added to reset the data of the app

1.0