5. Multiple Linear Regression is appropriate for: Predicting whether a drug is effective for a patient based on her characteristic Predicting tomorrow's rainfall amount based on the wind speed and temperat Predicting the sales amount based on month.

Multiple Linear Regression is appropriate for:<br /><br />1. Predicting whether a drug is effective for a patient based on her characteristics.<br />2. Predicting tomorrow's rainfall amount based on the wind speed and temperature.<br /><br />Multiple Linear Regression is not appropriate for:<br /><br />3. Predicting the sales amount based on the month.<br /><br />Explanation:<br /><br />1. Multiple Linear Regression can be used to predict whether a drug is effective for a patient based on her characteristics. This is because the effectiveness of a drug can be influenced by multiple factors, such as the patient's age, weight, medical history, and other characteristics. By using Multiple Linear Regression, we can model the relationship between these factors and the drug's effectiveness.<br /><br />2. Multiple Linear Regression can also be used to predict tomorrow's rainfall amount based on the wind speed and temperature. This is because the amount of rainfall can be influenced by multiple factors, such as the wind speed, temperature, and atmospheric pressure. By using Multiple Linear Regression, we can model the relationship between these factors and the rainfall amount.<br /><br />3. However, Multiple Linear Regression is not appropriate for predicting the sales amount based on the month. This is because the sales amount can be influenced by multiple factors, such as the product's price, marketing efforts, and economic conditions. Additionally, the sales amount can be influenced by non-linear factors, such as seasonal trends and promotional activities. Therefore, a more advanced regression model, such as Polynomial Regression or Time Series Analysis, may be more appropriate for predicting the sales amount based on the month.