The correct answer is: C. both multicollinearity & overfitting
Multicollinearity is a condition in which two or more independent variables are highly correlated with each other. This can cause problems in multiple regression, as it can make it difficult to determine which independent variable is actually causing the change in the dependent variable.
Overfitting is a condition in which a model fits the training data too closely, and as a result, does not generalize well to new data. This can happen when a model is trained on a small amount of data, or when the data is noisy.
Both multicollinearity and overfitting can lead to inaccurate results in multiple regression. It is important to be aware of these problems and to take steps to avoid them.
Here are some additional details about each of the options:
- A. Multicollinearity is a condition in which two or more independent variables are highly correlated with each other. This can cause problems in multiple regression, as it can make it difficult to determine which independent variable is actually causing the change in the dependent variable. For example, if you are trying to predict the price of a house, and you include both the size of the house and the number of bedrooms as independent variables, you may find that the two variables are highly correlated. This means that it will be difficult to tell whether the change in the price of the house is due to the change in the size of the house, or the change in the number of bedrooms.
- B. Overfitting is a condition in which a model fits the training data too closely, and as a result, does not generalize well to new data. This can happen when a model is trained on a small amount of data, or when the data is noisy. For example, if you are trying to predict the price of a house, and you train your model on a dataset of only 10 houses, your model may fit the data very well, but it will not be able to generalize well to new houses. This is because the model has learned the specific details of the 10 houses in the dataset, but it has not learned the general patterns that apply to all houses.
- C. Both multicollinearity & overfitting can lead to inaccurate results in multiple regression. It is important to be aware of these problems and to take steps to avoid them. There are a number of ways to deal with multicollinearity, such as using ridge regression or principal component regression. There are also a number of ways to deal with overfitting, such as using cross-validation or regularization.