In univariate regression, how is future yield estimated?

In univariate regression, the estimation of future yield is achieved by utilizing one independent variable and one dependent variable. This method focuses on the relationship between the two variables, where the independent variable (often referred to as the predictor) is used to predict the value of the dependent variable (the outcome). The simplicity of univariate regression allows for clear analysis and direct interpretation of how changes in the independent variable impact the dependent variable's yield.

By establishing this relationship, a model can be created that provides estimates for future values based on the observed data. For example, if the independent variable is a measure of time, univariate regression can show how yield changes over time based on historical data.

In contrast, options that involve multiple independent variables or complex statistical models relate to multivariate approaches, which are not applicable here since the question specifically mentions univariate regression. Additionally, while examining historical average trends is important for understanding data, it does not involve the systematic approach used in regression analysis to create predictive models. Thus, the core of univariate regression is inherently tied to the use of a single independent variable, making it the foundation for estimating future yields.