In testing a Quadratic effect, we compare Adj R2 from simple regression model to which of the following from the quadratic model?
In testing a quadratic effect, we compare the adjusted R-squared (Adj R2) from a simple regression model to the Adj R2 from the quadratic model.
In testing a quadratic effect, we compare the adjusted R-squared (Adj R2) from a simple regression model to the adjusted R-squared from the quadratic model.
To compare the performance of a simple regression model to a quadratic model in terms of the Quadratic effect, you can compare the adjusted R-squared values. The adjusted R-squared is a statistical measure that indicates the proportion of the variance in the dependent variable that is explained by the independent variable(s) in the model, while also considering the number of predictors included.
Here's how you can obtain the adjusted R-squared values for both models:
1. Simple Regression Model: Fit a simple linear regression model with the dependent variable and the independent variable of interest. Calculate the R-squared value and note it down.
2. Quadratic Model: Fit a quadratic model that includes the independent variable as well as its square term. This can be done by creating a new additional variable equal to the square of the independent variable. Fit the quadratic model and calculate the adjusted R-squared value.
Once you have the adjusted R-squared values, you can compare them to evaluate the improvement of the quadratic model over the simple regression model. Typically, if the adjusted R-squared of the quadratic model is larger than that of the simple regression model, it suggests that the quadratic effect is significant and the quadratic model provides a better fit to the data.