Description: Univariate regression is a statistical method used to analyze the relationship between a dependent variable and a single independent variable. This type of analysis is fundamental in statistics and is used to predict the behavior of the dependent variable based on the values of the independent variable. In simple terms, the goal is to find a function that best fits the observed data, allowing for predictions and understanding the relationship between the variables. Univariate regression is commonly represented by a straight line on a graph, where the independent variable is placed on the X-axis and the dependent variable on the Y-axis. This approach is particularly useful in situations where one wants to simplify the analysis and is confident that only one variable influences the outcome. Through univariate regression, parameters such as the slope and intercept of the regression line can be calculated, providing valuable information about the relationship between the variables. Additionally, metrics like the coefficient of determination (R²) can be evaluated, indicating how well the model fits the data. In summary, univariate regression is a powerful tool for data analysis, enabling researchers and analysts to gain meaningful insights from datasets with a single independent variable.
