A baseball team's pricing department would like to determine the price to sell each ticket to each game. This description is simplified from its real complexity; if you're an expert in the ticket pricing industry, please do not rely on your expertise to fill in all the extra complexity.

Currently there is a single price for each section's tickets - for example, to sit in Section 110 cost the same amount (75$) at every one of the team's 81 home games. The VP of pricing believes this leads to two kinds of bad outcomes. For some games(for example, a weekday afternoon game against a bad opponent), the seat never gets sold because nobody wants to pay that much, and for other games(for example, a Saturday night game against a very good opponent) all the seats in the sections are sold for $75 but could've been sold for more.

The VP of Pricing has decided to use analytics to determine the price for a ticket in each section at each game, based on the game's characteristics(day, time, opponent, etc.) and the ticket price.

At first, there is very little data, because each section's tickets have been sold at a single price for all 81 games.

After data has been collected over five years(more than 400 games with a wide range of prices), the VP of prices would like to have a tool built to help with pricing decisions.

Select all of the models/approaches the pricing department could use to estimate the number of tickets in a section that can be sold at a specific price, based on characteristics of the game(day, time, opponent, etc).

a.) ARIMA

b.)Discrete-Event Simulation

c.) K-nearest neighbour Regression

d.) Linear Regresion

e.) Logistic Regression