Say you are tasked with analyzing how well a model fits the data given. You want to determine a relationship between two variables.

What is the downside of only using the R-Squared $(R^2)$ value to do so?

In the context of regression analysis, why is it misleading to rely solely on a high R-squared ($R^2$) value to infer a causal relationship between variables?

In the context of regression analysis, why is it misleading to rely solely on a high R-squared ($R^2$) value to infer a causal relationship between variables?

Using R Squared I

A high $R^2$ value indicates a large amount of unexplained variance in the model

A high $R^2$ value indicates a strong correlation, but correlation does not imply causation

A high $R^2$ value implies that the model's parameters are statistically insignificant

A high $R^2$ value means the regression model is too complex and overfitted

A high $R^2$ value can only be obtained when there is a negative relationship between variables

Which of the following best describes the R-squared ($R^2$) value in the context of a regression model?

Which of the following best describes the R-squared ($R^2$) value in the context of a regression model?

Using R Squared II

It measures the degree of linearity in the relationship between dependent and independent variables

It indicates the number of independent variables included in the model

It measures the proportion of the variance in the dependent variable that is predictable from the independent variables

It represents the number of data points in the regression model

It provides a measure of the causal relationship between the dependent and independent variables

What is a limitation of using R-squared ($R^2$) as the only metric to evaluate a regression model's fit?

What is a limitation of using R-squared ($R^2$) as the only metric to evaluate a regression model's fit?

Using R Squared III

It only applies to models with a positive correlation between variables

It inaccurately calculates the proportion of the variance when the dependent variable is binary

It does not account for the number of predictors in the model, leading to overfitting with excessive parameters

It cannot be used for models with more than two independent variables

It increases when the size of the dataset decreases

Using R Squared

Comments