In this blog post, we take a look at Ordinary Least Squares (OLS) regression, and see how it can be used to predict ticket sales for concerts.
Ordinary Least Square regression is a statistical method used to estimate the relationship between a dependent variable and one or more independent variables, with the goal to find the line of best fit that minimizes the sum of the squared differences between the observed values and the predicted values.
The relationship between the dependent, or response, variable, and the independent/predictor/explanatory variable(s) is modeled using the linear equation,
\(y = β_0 + β_1X_1 + B_2X_2 +... + ε\) where \(y\) is the dependent variable, \(X\)s are the independent variables, \(β\) are the regression coefficients, and \(ε\) is the error term.
In the concert industry, promoters might use some of the following data from past concerts in a given city to fit a model:
This by no means is the extent of data that could be used to predict concert ticket prices, but some examples of data that might be used to build an effective model.
Fitting a linear model using these predictors would allow promoters to determine which variables are statistically significant to Tickets Sold, and adjust the model accordingly. While this blog post is specifically concerned with ordinary least squares regression, other options, including weighted least squares, transformations, autocorrelation, will be covered in future posts.
Ordinary Least Squares regression offers predictive ability that has business uses across many applications. In the music industry/concert space, it can allow users the ability to predict the outcome of events based on available data.