Select the correct statement(s) about using Linear Regression for classification:

1. Linear Regression is not typically used for classification: Linear Regression is a type of predictive modeling technique which is used to find the relationship between a dependent variable (target) and one or more independent variables (predictors). It is primarily used for regression problems, where we need to predict a continuous outcome variable.

2. Linear Regression can be used for binary classification: While it's not common, Linear Regression can be used for binary classification problems. However, it may not be the best choice as it assumes that the data is linearly separable which is not always the case in real-world scenarios.

3. Linear Regression does not provide probabilities: Unlike Logistic Regression which provides probabilities and is widely used for classification problems, Linear Regression does not output probabilities. It outputs a continuous outcome which can be thresholded to classify instances.

4. Linear Regression is sensitive to outliers: Linear Regression is sensitive to outliers in the data. An outlier can significantly change the regression line, thus impacting the performance of the model on the classification task.

5. Linear Regression assumes a linear relationship: Linear Regression assumes that there is a linear relationship between the predictors and the target variable. This assumption may not hold true for many real-world classification problems where the relationship between predictors and target variable could be complex and non-linear.

In conclusion, while it's technically possible to use Linear Regression for classification tasks, it's not typically the best choice due to the reasons mentioned above.