Chartered Financial Analyst (CFA) Practice Exam Level 2

What issue is associated with overfitting in regression analysis?

1. Too few variables included in the model

2. Using too many X-variables, causing random noise to be perceived as a pattern

3. Failing to achieve a significant result

4. Overestimation of model parameters

The correct answer is: Using too many X-variables, causing random noise to be perceived as a pattern

Overfitting in regression analysis occurs when a model is excessively complex, typically by including too many independent variables relative to the amount of data available. This complexity allows the model to capture not only the underlying patterns in the data but also the random noise. As a result, the model becomes tailored to the specific observations in the training set, leading to poor predictive performance on new or unseen data. When too many predictor variables are included, the model may interpret random fluctuations in the dataset as if they are significant patterns. Consequently, while the model shows a high degree of accuracy for the training data, it fails to generalize effectively, which is the fundamental issue associated with overfitting. This defeats the purpose of regression analysis, which aims to predict outcomes based on the relationships between variables rather than fitting noise. The other options do not accurately describe the primary concern with overfitting. Having too few variables could lead to underfitting, where the model is too simplistic to explain the data effectively. Failing to achieve a significant result does not inherently indicate overfitting but rather suggests a lack of a meaningful relationship between the variables in the model. Lastly, overestimation of model parameters can occur as a result of overfitting, but it is more