This course consists of two sections: Section 1 demonstrates linear regression to model the linear relationship between a response and predictor(s) when both the response and predictors are continuous ...

The work revisits the autocovariance function estimation, a fundamental problem in statistical inference for time series. We convert the function estimation problem into constrained penalized ...

Linear regression is a powerful and long-established statistical tool that is commonly used across applied sciences, economics and many other fields. Linear regression considers the relationship ...

Logistic regression is a powerful statistical method that is used to model the probability that a set of explanatory (independent or predictor) variables predict data in an outcome (dependent or ...

Linear regression, also called simple regression, is one of the most common techniques of regression analysis. Multiple regression is a broader class of regression analysis, which encompasses both ...