For the purpose of predicting revenue at the end of two years after adding drama genre movies to their portfolio, “CMC Pictures” would benefit most from using Multiple Regression. This analytical technique is specifically designed to examine the relationship between multiple independent variables and a dependent variable. Here’s why it’s suitable:

• Multiple Variables: Multiple regression allows the consideration of various factors that could influence revenue, such as the number of movies released, marketing spend, overall market trends, seasonal effects, and the specific impact of introducing drama movies.
• Predictive Capability: Unlike the other listed methods, multiple regression not only assesses the strength of relationships but also can be used to make predictions based on those relationships. It can forecast future revenue based on input values for the variables included in the model.
• Quantitative Analysis: It provides a quantifiable way to evaluate how significant each variable (like adding drama genre movies) is in contributing to changes in revenue, adjusting for other factors in the model.

In contrast:

• Correlation would only indicate whether there’s a relationship between two variables, like revenue and the addition of drama movies, but it wouldn’t account for other influencing factors or allow for precise revenue predictions.
• T-test and ANOVA are more about testing differences between groups, such as comparing average revenues before and after introducing drama movies, rather than predicting specific future values.

Therefore, multiple regression is the most comprehensive and appropriate tool for CMC Pictures to predict their revenue after expanding their portfolio to include drama genre movies.