Refers to a general estimation technique that selects the parameter value to minimize the squared difference between two quantities, such as the observed value of a variable, and the expected value of that observation conditioned on the parameter value.
I am doing a computer simulation for some physical system of finite size, and after this I am doing extrapolation …
python numpy linear-regression least-squares extrapolationI'm running a regression on census data where my dependent variable is life expectancy and I have eight independent variables. …
r regression least-squaresI have been fitting linear least-squares polynomials to data using the polyfit function in matlab. From what I read, this …
matlab curve-fitting polynomial-math least-squaresI am looking for a C# implementation of the Levenberg–Marquardt algorithm for non-linear least squares fit.
c# least-squares levenberg-marquardtI am trying to find a java code to compute the least squares solution (x) in the Ax=b equation. …
java least-squaresI have a set of points (x,y) and I need to find the line of best-fit that passes through …
algorithm matlab numerical curve-fitting least-squaresI am trying to learn an ordinary least squares model using Python's statsmodels library, as described here. sm.OLS.fit() …
python least-squares statsmodelsI am trying to fit my data to a function. I've been using this sample code as a guide http://…
python numpy scipy least-squaresI know how to solve A.X = B by least squares using Python: Example: A=[[1,1,1,1],[1,1,1,1],[1,1,1,1],[1,1,1,1],[1,1,0,0]] B=[1,1,1,1,1] X=numpy.linalg.lstsq(…
python numpy matrix least-squareslstsq tries to solve Ax=b minimizing |b - Ax|. Both scipy and numpy provide a linalg.lstsq function with …
python numpy scipy least-squares