Select Page

There are several forms of regression analysis, each meeting a distinct set of analytical requirements. The most popular kind, linear regression, simulates linear correlations between independent and dependent variables. This is extended to polynomial functions using polynomial regression. Using the logistic function, logistic regression forecasts categorical outcomes. Regularization is included into Ridge and Lasso regressions to avoid over fitting. Data sorted by time is analyses using time series regression. Multiple independent variables are included in multivariate regression. Data are fitted to nonlinear models using nonlinear regression. The main goal of quartile regression is to estimate conditional quartiles. Techniques from Bayesian inference are included into Bayesian regression. Lastly, outliers cannot affect robust regression methods. The types are selected according to the goals of the research and the type of data, each of which has unique benefits.

SAS Online Training Institute, Power BI, Python Pune, India

Are you finding Top SAS Training Institute, Power BI, Python Online Training Classes, R Programming Language Training Institute in India? We are Best SAS, Power BI, Python, R Training Online Course in Pune, Delhi India. if yes then contact with Durga Online Trainer