Some of you may have come across a growing number of publications in your field using an alternative paradigm called Bayesian statistics in which to perform their statistical analyses. The goal of ...

In this short course we will provide an introduction to linear regression and how to utilize it in R. We will cover the theory of linear regression as well as demonstrating how to use R to make and ...

Bayesian quantile regression and statistical modelling represent a growing paradigm in contemporary data analysis, extending conventional regression by estimating various conditional quantiles rather ...

If you have experience with R or want a quick way to generate a regression with statsmodels using a pandas DataFrame, you can use R-style formulas. First, you need to import statsmodels and its ...

Fuzzy regression models extend traditional statistical regression by integrating fuzzy set theory to better handle imprecision and uncertainty inherent in many real-world data sets. These models ...

• Background and Aims Most current thermal-germination models are parameterized with subpopulation-specific rate data, interpolated from cumulative-germination-response curves. The purpose of this ...

This is a preview. Log in through your library . Abstract We consider quadratic regression models where the explanatory variable is measured with error. The effect of ...

When you use the statistical analysis features in Excel, you are leveraging one of the most powerful tools available for data manipulation and interpretation. Excel is not just a spreadsheet ...