R-squared, also known as the coefficient of determination, measures the proportion of variance in the dependent variable that is predictable from the independent variable(s) in a regression model. It ranges from 0 to 1, where a higher value indicates a better fit of the model to the data, but it does not imply causation or model accuracy in prediction outside the sample data.

R-squared

The coefficient of determination, denoted as R², measures the proportion of variance in the dependent variable that is predictable from the independent variable(s) in a regression model. It provides an indication of how well the model fits the data, with values closer to 1 indicating a stronger explanatory power of the model.

coefficient of determination

proportion of variance

A dependent variable is the outcome factor that researchers measure in an experiment or study, which is influenced by changes in the independent variable. It is crucial for determining the effect of the independent variable and understanding causal relationships in research settings.

dependent variable

An independent variable is a factor in an experiment or study that is manipulated or controlled to observe its effect on a dependent variable. It is essential for establishing causal relationships and is typically plotted on the x-axis in graphs.

independent variable

A regression model is a statistical tool used to understand the relationship between a dependent variable and one or more independent variables, allowing for predictions and insights into data trends. It is essential in various fields for forecasting, determining causal relationships, and optimizing outcomes based on historical data.

regression model

better fit

model to the data

Causation refers to the relationship between two events where one event is the result of the occurrence of the other event; it is a fundamental principle in scientific inquiry for establishing cause-and-effect relationships. Understanding causation is crucial for making predictions, formulating theories, and implementing effective interventions in various fields such as medicine, economics, and social sciences.

causation

Model accuracy is a metric used to evaluate the performance of a predictive model by calculating the proportion of correct predictions made out of all predictions. It is crucial for determining how well a model generalizes to unseen data but should be considered alongside other metrics to avoid misleading interpretations, especially in imbalanced datasets.

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