Decision Tree is a machine learning algorithm, not a statistical model.

The goal of model selection is to find the model that will perform best on unseen data, not just the data that was used to train the model.

R-squared is a measure of how well the model fits the data, ranging from 0 to 1, with higher values indicating a better fit.

The key difference between parametric and non-parametric models is that parametric models assume a specific distribution for the data, while non-parametric models do not.

ARIMA is a widely used time series analysis technique that combines autoregressive, integrated, and moving average components.

Regularization is a technique used to reduce overfitting, which occurs when a model learns the training data too well and starts to make predictions that are too specific to the training data.

Logistic Regression is a widely used statistical modeling technique for binary classification problems, where the output is a probability between 0 and 1.

The key difference between supervised and unsupervised learning models is that supervised learning models are trained on data that has been labeled with the correct output, while unsupervised learning models are trained on data that has not been labeled.

K-Means Clustering is a widely used statistical modeling technique for clustering data, where the data is divided into a specified number of clusters.

Cross-validation is a technique used to estimate the accuracy of a model on unseen data by dividing the data into multiple subsets and training and evaluating the model on different combinations of these subsets.

ARIMA is a widely used statistical modeling technique for time series forecasting, where the future values of a time series are predicted based on its past values.

The key difference between deterministic and stochastic models is that deterministic models make predictions with certainty, while stochastic models make predictions with uncertainty due to the presence of random variables.

Kaplan-Meier Estimator is a widely used statistical modeling technique for survival analysis, where the survival function of a population is estimated from censored data.

Model diagnostics are used to identify potential problems with a model, such as overfitting, underfitting, or incorrect model assumptions.

Geographically Weighted Regression (GWR) is a widely used statistical modeling technique for spatial data analysis, where the relationship between variables is allowed to vary across space.