To identify a trend in the dramatic intensity scores described over the last section, they are put in a more mathematically measurable form, that is, a frequency distribution table below: The table above shows the score value through the scale (1- 10) with the number of scenes in the movie that have been rated with the respective score values. Since the number of scenes in the movie is 16 the lower frequency column adds up to 16. Below is the graph that shows that above plotted values: To derive a more defined interpretation for the graph, it is further plotted as a bar graph with score intervals of size two.

The scene distribution is plotted as calculated percentages of the scenes that fall under each score interval. This graph shows: 1. 0% of scenes have a DI score in the range of 0-2 2. 12. 5% of the scenes have a score between 2-4 3. 18. 75% of the scenes are rated between 4-6 4. 43. 75% of the scenes have a score between 6-8 5. 25% of the scenes are rated between 8-10 Thus a majority of the movie scenes have a very high rating of 6-8, while a significant portion of the scenes have an almost perfect score or 8-10.

This graph strongly indicates that the movie can be classified as highly interesting. Some observations that can be made from the graph: The graph is skewed towards the left. That is, the graph is asymmetrically spread around the average value with the majority distribution concentrated towards the left. The tails of the graphs at either end taper sharply, though much more sharply at the right end, as compared to the left end. This indicates, less number of scenes with a low DI score.

The observations from the graph suggest that graphs of interesting movies will be heavier, or skewed towards the left more than the right, that is more scenes will have higher scores plotted towards the positive direction of the x-axis. Conclusion The evidence of this study suggests that movies can be identified as interesting on the basis of the Dramatic Intensity Distribution graphs. The graph of an interesting movie will tend to be skewed towards the left, since it will have fewer number of low intensity dramatic scenes.

Of course this will not apply to movies of a genre where drama is understated. In such cases the parameter will have to be aptly determined. The dramatic intensity level is however a convenient parameter of measure that can be applied to most popular movies. Another important implication of this graph is the possible shape of graphs of movies that are not interesting. Movies, which are not interesting will have more scenes with lower DI scores. Thus the graph will be skewed towards the right, with a fewer number of scenes with high dramatic intensity scores.

The graph is not an absolute determinant, as there may be cases where there are a number of scenes that have a short runtime but have extremely high DI scores. And most of the movie constitutes long scenes with low or average DI scores. In such cases the movie may not be very interesting on the whole. The run-time of scenes has to be taken into considerations to determine the actual quotient of interesting scenes in the movie. An interesting extension of this study would be to collect the graphs of a large sample set of popular, interesting and successful movies and compare their shapes.

There may be an interesting insight to a possible threshold level of skew or pattern to the graph of above average movies. It would also be interesting to compare multiple parameters for different movies to explore their interrelationships. To sum up, this study shows that a combination of economic and data analysis theories can be combined to discover common patterns and traits in successful Hollywood and even world cinema. While some of these models can be region specific, they also can be built with universal parameters.

These models can contribute to the understandings and studies of scholars of cinema with respect to contemporary and classic trends and the evolving choices of the average viewer. Alteration of parameters from the basic can be applied to study different niche crowd. The industry can also use such models to explore potential projects.

References

Crawford, W. (1990, October 29). Trends in Hollywood Cinema: Is cinema using ideas or merely aping trends? The Washington Post, p. A3. Clarkson, P. M. , & Lloyd, E. S. (1990).

Analytical measurement: Processes of learning and invention in the evolution and development of mathematical concepts. In S. T. Walker & K. R. Foquel (Eds. ), Mathematical analysis in economic trends: Comparative developmental perspectives (pp. 540-578). Cambridge: Cambridge UniversityPress. Doumpous, M. , C. Zopounidis. (2002, August 31). Multicriteria Decision Aid Classification Methods (Applied Optimization). Springer. Eckholm, E. (1985, June 25). Studying Cinema: Entertainment science. The New York Times, pp. C1, C3. Gibbons, A. (1991). Tools for analyzing probability data: Skews and Kurtosis, 251, 1561-1562.