There are several types of statistical regression models that can be used to analyze data, including:

1. Linear Regression: This is a commonly used regression model that relates a continuous dependent variable to one or more independent variables. It assumes that the relationship between the dependent variable and independent variables is linear.
2. Multiple Regression: This model is an extension of linear regression that includes more than one independent variable to predict the dependent variable. It is used when there are multiple predictors that may affect the outcome variable.
3. Logistic Regression: This is a regression model used to predict binary outcomes, where the dependent variable is categorical (e.g., yes or no). It models the probability of the dependent variable taking a particular value based on one or more independent variables.
4. Poisson Regression: This model is used when the dependent variable is a count variable, such as the number of times an event occurs in a given time period. It models the expected count of an event based on one or more independent variables.
5. Cox Regression: This model is a type of survival analysis used to model the time to an event, such as death or failure. It models the hazard rate of the event occurring based on one or more independent variables.
6. Nonlinear Regression: This model is used when the relationship between the dependent variable and independent variables is not linear. It can take various forms, such as exponential, logarithmic, or power function.

In machine learning, there are various types of regression models that can be used to analyse data, including:

1. Linear Regression: This model is commonly used in machine learning for predicting continuous outcomes. It involves fitting a linear equation to the data with one or more independent variables.
2. Polynomial Regression: This model is an extension of linear regression that involves fitting a polynomial equation to the data. It is used when the relationship between the dependent and independent variables is not linear.
3. Ridge Regression: This model is a type of linear regression that is used when there is multicollinearity (high correlation) between the independent variables. It involves adding a penalty term to the regression equation to reduce the effect of multicollinearity.
4. Lasso Regression: Similar to Ridge Regression, Lasso Regression is also used when there is multicollinearity between independent variables. However, instead of adding a penalty term, it involves shrinking some of the coefficients to zero, leading to a more parsimonious model.
5. Elastic Net Regression: This model is a combination of Ridge and Lasso regression, used when there are a large number of independent variables. It involves adding both penalty terms to the regression equation, which balances the strengths of both methods.
6. Decision Tree Regression: This model involves constructing a decision tree from the data and using it to make predictions. It is used when the relationship between the dependent and independent variables is not linear and can handle both categorical and continuous data.
7. Random Forest Regression: This model is an ensemble learning method that uses multiple decision trees to make predictions. It is used when there are multiple independent variables and can handle missing data and outliers.