Movie Industry Project Proposal

Economy There are different types of data, including time series and cross-sectional data. Time series data consists of measurements of the same variable over time. Marketers will often use time-series data, for example if a company is new and building its brand, it can take surveys on a monthly basis for its first few years to measure how many people are aware of its brand. This would be a dependent variable, and the company's marketing efforts the independent variable.

Cross-sectional data is a set of data that may be in the same time, but examines different populations. The same company, in its marketing study, might look at brand recognition to gauge the effectiveness of its marketing in different populations -- such as different age cohorts, or in different countries. This information can be useful to the marketer in studying its reach to those different populations. A multiple regression considers the effects of multiple independent variables on a single dependent variable.

If there is only one independent variable, it is a linear regression. A multiple regression is a common tool in marketing, because it seeks to derive explanatory factors in complex, real-world environments. For example, suppose a researcher wanted to analyze box office receipts for new movies. There are a number of factors that go into the success of a movie -- how much was spent on making the movie, advertising budget, number of theaters, whether the movie is star-driven, and any number of other factors.

A multiple regression can explain the influence of each of these different factors, which would in turn help decision-makers set out budgets, knowing the cost-benefit analysis of the different contributing factors to a movie's success. For this project, the analysis can focus on the production budget of the film as the one independent variable, and the dependent variable will be the North American box office take.

There are obviously several other independent variables, but the focus here will be on identifying the link between production budgets and box office receipts, to answer a research question of "Do big budget movies perform better at the box office than small budget movies?" There are a number of different sources for this information. The project can only reasonably use films for which this information has been made public, but also can only be applied to films that have finished their theatrical run.

The study will need to be cross-sectional in nature, the time period of films discussed must be similar, in order to discount the effects of inflation on the box office figures, which are reported in nominal (not real) terms. Thus, films from 2014 will be considered for this work. There were more than 30 films released in that year, so the samples will need to be chosen at random from a pool that includes all releases, both successful and unsuccessful.

A data set of thirty was gathered, using random number generation and statistics provided by an industry website, Box Office Mojo. The return at the box office is a key element of the ROI for a movie (along with foreign receipts, video sales, licensing and non-production costs). This is, however, a standard industry measure to look at the ROI specifically through.