Provide two examples, one in which you should use simple linear regression and one in which you should use multiple regression.
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Example 1: Simple Linear Regression
Suppose a clothing company wants to estimate the relationship between the average monthly temperature and their monthly sales. They collect data on the average monthly temperature and the corresponding monthly sales for the past few years. Here, simple linear regression can be used as there is only one predictor variable (average monthly temperature) that is believed to have a linear relationship with the response variable (monthly sales). The model can help the company understand how temperature affects their sales and make predictions based on future temperature forecasts.
Example 2: Multiple Regression
Consider a study aiming to predict housing prices based on various factors such as the size of the house, the number of bedrooms, the location, and the age of the property. In this case, multiple regression would be more appropriate. Each of these predictor variables (house size, number of bedrooms, location, and age) can contribute to the price in a different way, and multiple regression allows us to understand their individual and collective effects. By using multiple regression, we can build a model that considers all these variables simultaneously and predicts house prices more accurately.
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