Favorite Day to Eat Out A survey was conducted by Wakefield Research in which participants were asked to disclose their favorite night to order takeout for dinner. The following data are based on their results.

c. If you own a restaurant, which day would you purchase an advertisement in the local newspaper? Are there any days you would avoid purchasing advertising space?

Verified step by step guidance

1. Step 1: Organize the data by counting the frequency of each day mentioned in the table. This means tallying how many times each day of the week appears as a favorite night to order takeout.
2. Step 2: Create a frequency table listing each day of the week (Monday through Sunday) alongside the number of times it appears in the survey responses.
3. Step 3: Identify the day with the highest frequency. This day represents the most popular night for ordering takeout according to the survey.
4. Step 4: Identify the days with the lowest frequencies or those that appear very infrequently. These days would be less effective for advertising since fewer people prefer them for ordering takeout.
5. Step 5: Based on the frequency analysis, decide to purchase advertisement space on the day(s) with the highest frequency to maximize reach, and avoid days with low or zero frequency to save advertising costs.

Key Concepts

Frequency Distribution

A frequency distribution summarizes data by showing how often each value occurs. In this case, counting how many times each day appears helps identify the most and least popular days for ordering takeout. This is essential for making decisions based on the data.

Mode

The mode is the value that appears most frequently in a data set. Here, the mode indicates the favorite day to order takeout, which is crucial for targeting advertising efforts effectively to maximize customer reach.

Data-Driven Decision Making

Using data to guide business decisions ensures resources are allocated efficiently. By analyzing survey results, a restaurant owner can choose the best day to advertise and avoid days with low customer interest, optimizing marketing impact.