Linear regression is a statistical approach for modeling the connection between one or more independent variables and a dependent variable. It assumes a linear connection and seeks the best-fit line with the smallest sum of squared discrepancies between observed and forecasted values. Linear regression produces a linear equation, which may be used to make predictions or to understand the strength and direction of the correlations between variables. It is commonly used in economics, finance, and machine learning for tasks including as forecasting, trend analysis, and feature selection.
In 2018, a dataset collecting health-related data from numerous US states was compiled. The collection includes 3,143 diabetes samples, 3,142 nonspecific category samples, 363 obese samples, and 1,370 inactivity samples. certain samples are likely to provide information on aspects such as prevalence rates, demographics, or risk factors for certain health disorders. This dataset’s analysis can aid in identifying trends and links between diabetes, inactivity, obesity, and geographic areas in the United States.