In a real-world application, which of the following scenarios is best suited for

In a real-world application, which of the following scenarios is best suited for linear regression?

In a real-world application, which of the following scenarios is best suited for linear regression?

Step 1: Understand what linear regression is. It is a method used to predict a continuous outcome based on one or more input features.
Step 2: Identify the type of outcome you want to predict. Linear regression is best for predicting continuous values, not categories.
Step 3: Think of examples of continuous outcomes. For instance, house prices, temperature, or weight are all continuous outcomes.
Step 4: Consider the features that might influence the outcome. For house prices, features could include the size of the house, location, and number of bedrooms.
Step 5: Conclude that if you want to predict a continuous outcome based on various features, linear regression is a suitable choice.

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