AI Engineering Degree Practice Exam 2025 - Free AI Engineering Practice Questions and Study Guide

Multiple Linear Regression is a statistical technique used to model the relationship between a dependent variable and multiple independent variables. It is particularly useful when you want to understand how several factors impact a particular outcome and is typically applied in scenarios where the dependent variable is continuous.

In the context of predicting the production of apples in an orchard based on temperature and rainfall, Multiple Linear Regression is the ideal approach as you are looking to quantify the contribution of multiple independent variables (temperature and rainfall) to a specific outcome (apple production). This approach allows one to assess how changes in temperature and rainfall affect apple yield collectively, providing a meaningful forecast reflecting these influences.

In contrast, predicting stock market trends involves complex interdependencies and often non-linear relationships, which may be better suited for different modeling techniques. Classifying emails as spam or not falls into the realm of classification problems, where different algorithms such as logistic regression or decision trees are more appropriate. Forecasting weather conditions typically uses time-series analysis or other predictive modeling techniques that can capture the temporal dynamics of weather data rather than focusing on relationships across multiple independent variables in a straightforward manner as done in Multiple Linear Regression.