The error term can be thought of as the composite of a number of minor influences on the target variable that cannot be captured by the model. As the number of these minor influences gets larger, the distribution of the error term tends to approach the normal distribution. This tendency is called the Central Limit Theorem. The t-test and F-test are not applicable unless the error term is normally distributed.
Articles in this section
- Evaluation Metrics in Keras
- Covariance in Time Series
- How to merge two dataframes x_test and y_pred?
- What is pickling and unpickling?
- What is Hierarchical Clustering?
- Bottoms-up approach in Hierarchical Clustering
- Random Forest - Low bias and Low variance
- What is Euclidean And Manhattan distances in KNN?
- Is it possible to conclude over-fitting over train data only?
- Identification of biased train and test scores