for issues related to linear regression modelling approach
val tvalues: Array[Double] = Array(1.866393526974307, 2.864048126935307, 4.032486069215076, 7.876169953355888, 4.875333799256043, 14.316322626848278) val pvalues: Array[Double] = Array(0.064020056478447, 0.004808399479386827, 8.914865448939047E-5, 7.489564524121306E-13, 2.8363794106756046E-6, 0.0) I have two Arrays as above, i …
arrays scala linear-regression spark-dataframeI am performing multiple regressions on different columns in a query file. I've been tasked with extracting certain results from …
r linear-regression p-valueI am working with sklearn and specifically the linear_model module. After fitting a simple linear as in import pandas …
python scikit-learn linear-regression varianceI want to compute AIC for linear models to compare their complexity. I did it as follows: regr = linear_model.…
python linear-regressionIf we want to search for the optimal parameters theta for a linear regression model by using the normal equation …
python numpy matrix linear-algebra linear-regressionIn the sklearn.linear_model.LinearRegression method, there is a parameter that is fit_intercept = TRUE or fit_intercept = FALSE. …
python scikit-learn linear-regressionI have been trying to figure out how the subset argument in R's lm() function works. Especially the follwoing code …
r linear-regression lmI've used lm() to fit multiple regression models, for multiple (~1 million) response variables in R. Eg. allModels <- lm(…
r regression linear-regression lm mlmI'm working on machine learning problem and want to use linear regression as learning algorithm. I have implemented 2 different methods …
matlab machine-learning linear-regression gradient-descentI want to use a linear regression model, but I want to use ordinary least squares, which I think it …
statistics linear-regression spss least-squares