Question

Write a matrix to organize the information about a video store's movies. Label each row and column. Comedy: 25 new releases, 215 regular selections Drama: 30 new releases, 350 regular selections Horror: 26 new releases, 180 regular selections

Short answer

The matrix to organize the information about the video store's movies will look like: \[\begin{array}{ccc} & \text{New releases} & \text{Regular selections} \\ \text{Comedy} & 25 & 215 \\ \text{Drama} & 30 & 350 \\ \text{Horror} & 26 & 180\end{array}\]

Step-by-step solution

Define the matrix structure

A matrix consists of rows and columns and each cell in a matrix corresponds to a unique row and column. Since the information is on 'Comedy', 'Drama' and 'Horror' movies and each has 'New releases' and 'Regular selections', it is appropriate to construct a 3x2 matrix. The rows of the matrix will represent the genres of the movies and the columns will represent the type of the movie.

Fill in the entries for the matrix

Each entry in the matrix corresponds to the number of 'new releases' and 'regular selections' for a particular type of movie. The numbers can be filled in the corresponding cells for each row by reading off the details from the problem. For instance, the cell in the first row and first column of the matrix corresponds to the new releases for 'Comedy' movies which are 25, and so on.

Label the rows and columns

To indicate that the rows represent the genres and the columns represent the type of the movies, labels can be placed beside the matrix indicating the corresponding movie genres i.e., 'Comedy', 'Drama', 'Horror' adjacent to each row and the types 'New releases' and 'Regular selections' atop each column.

Key concepts

Matrix Representation

When we talk about matrix representation, we refer to a grid-like structure that systematically arranges numerical data. It's an incredibly powerful tool in algebra that displays data in rows and columns, making complex information more accessible and easily understandable.

Think of a matrix as a large table, with each 'cell' containing a specific piece of data. In our case, we have a video store that categorizes movies into genres and types. The matrix will have two dimensions: one for movie genres such as Comedy, Drama, and Horror, and the other for 'New releases' and 'Regular selections'. This structured representation allows for quick reference and efficient data handling, for tasks ranging from simple categorization to complex mathematical computations in algebraic operations.

Row and Column Labeling

The process of row and column labeling is vital as it gives meaning to the data within our matrix. It is how we identify what each entry in our matrix represents. Without proper labels, the numbers in our matrix could be confusing.

Consider a spreadsheet where the leftmost column typically lists the items, and the top row includes the categories. In a similar fashion, in our video store matrix, each row corresponds to a genre. We label these rows as ‘Comedy’, ‘Drama’, and ‘Horror’. For the columns, we label them as ‘New releases’ and ‘Regular selections’. These labels are like addresses for each cell of the matrix, guiding us precisely to the information we're looking for, whether it is the number of new comedy releases or regular horror selections available.

Categorization in Matrices

Matrices aren't just about numbers; they're also categorization champions. This approach works perfectly for organizing data that fits into clear groups and subgroups. Our video store's movie inventory is a prime example of this practice.

We can categorize movies by genre and type, creating a bin for each combination. With matrices, this organization process becomes streamlined. Each genre (a category) is a row, each type (a subgroup) is a column, and their intersection tells us the specific count of movies for that bin. It is like creating a map of the store’s movie collection, enabling us to pinpoint the location of each category and make decisions – say, for ordering new stocks or analyzing consumer preferences – efficiently and accurately. Effective categorization in matrices brings order to potentially chaotic data, fitting information into a system that's both easy to navigate and use as a basis for further analysis.