R Programming Analysis on the Relationship Between Film Budget and Total Returns

Problem Description:

Explore the relationship between the total returns generated by films and their budgets. The analysis is conducted on a dataset of films, and we are interested in understanding how film budget influences the total returns. We also examine the effect of time on this relationship by comparing film returns before and after the 2000s.

Solution

Appendix A: Questions

1. Cluster Analysis:
• In our initial analysis, we observed that the relationship between total returns from films and film budget is concentrated at the lower budget levels.
• Logarithmic transformation of both total returns and film budget magnifies this relationship.
• Remarkably, we discovered a strong positive correlation between these two variables in both the original and transformed forms.
2. Exploring Expensive Films:
• While examining the dataset, we identify the films with the highest budgets.
3. Statistical Significance:
• Utilizing a 0.05 confidence interval, we test whether there is sufficient evidence of a change in the relationship between total returns from films and film budget.
• Our analysis indicates that there is no significant change, as the p-values are 0.907 and 0.666.

Appendix A: Question 4 4. Analyzing Relationships:

1. Positive Relationship:We establish a positive relationship between total returns from films and film budget. An increase of one unit in the budget results in an increase of approximately 16.52 units in total returns.
2. Change Over Time (Pre-2000 to 2010s):The estimated change in total returns from films between the time periods before the 2000s (Pre-2000) and the 2010s is 0.9813931.
3. Change Over Time (Pre-2000 to 2010s):The estimated change in total returns from films between the time periods before the 2000s (Pre-2000) and the 2010s is 0.9877135.

Appendix B:

4. Interaction Analysis:
• We examine interaction plots between property type and the number of bedrooms.
• The plots reveal that the two lines intersect at a specific point, indicating a significant interaction between property type and the number of bedrooms.
5. Bedroom Analysis:
• We specifically explore the data for 3-bedroom properties.