Advanced Regression Analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. Regression analysis helps one understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed. In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function which can be described by a probability distribution.

Regression Analysis is widely used for prediction and forecasting, used to infer causal relationships between the independent and dependent variables, to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships. Regression analysis estimates the conditional expectation of the dependent variable given the independent variables. We have helped innumerable students through online help with Advanced Regression Analysis assignment. Our Experts have the capability to write the content on any referencing styles, while delivering all the projects & assignments are accompanied by substantiation documentation that helps the students in viva voce as well as in making the presentations over the topic. Our experts are well versed with the various referencing styles available in the academic scenario.

Students studying Statistical Computing can avail our help in completing their projects or assignments at a reasonable & minimal cost with quality par excellence in the following topics:

• Basics of Least Squares Regression
• Breakdown point, in, uence function
• Censored dependent variable
• Common Misconceptions about Fit
• Critiques of the Linear Regression Model
• Cross-validation for smoothing parameters
• Diagnosing linearity through residual plots
• Fixing non-linearity with data transformations
• Fraction of variance unexplained
• Generalized Additive and linear Models
• Heteroskedastic linear regression
• Inference for regression smoothers
• iterative re-weighted least squares
• Multinomial and ordered logit and probit
• Multivariate adaptive regression splines
• Multivariate normal distribution
• Nonparametric regression and Smoothing
• Ordinary Least Squares Regression
• Pearson product-moment correlation coefficient
• Regression discontinuity designs
• Tests for Nonlinearity and Interaction
• Theoretical issues in model searching
• Two-Predictor Model