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Function notation is a simple and effective way to describe relationships in mathematics. It uses a specific format to represent functions. Typically, it takes the form \(s(t)\), where "\(s\)" is the function name, and "\(t\)" is the variable representing time, in this case. The notation \(s(t)\) specifically helps to determine the output of the function based on the input variable \(t\).
For this exercise, \(s(t)\) is used to represent the average starting salary for MBA graduates in a specific year. The variable "\(t\)" denotes the year of interest, while "\(s(t)\)" gives us the corresponding salary value. This notation is very useful in clearly communicating how a specific input, such as a year, affects the outcome, like salary in this context.
This approach makes it easy to swap out different input values and see how the output changes. Understanding function notation is key to interpreting various real-world data scenarios, such as changes in salary over time.

MBA salaries refer to the starting salaries of graduates who have completed a Master of Business Administration program. This is an important indicator of how well the demand for these graduates translates into financial compensation.
In the exercise, MBA salaries are expressed in function notation to observe trends over different years. Salaries can vary based on several factors such as the university's reputation, the economic climate, and the industry sector. Often, starting salaries are used as a benchmark for assessing the return on investment for individuals considering an MBA.
Observing changes in MBA salaries over time can provide insights into:

• Economic conditions affecting the job market.
• Changes in demand for MBA graduates.
• General trends in the business and finance sectors.

Understanding these patterns is critical for prospective students and educational institutions.

Interpreting yearly salary values in function notation involves analyzing data for a specific time period. Each notation, like \(s(2006) = 95,400\), is an encapsulation of information about that particular year's economic conditions and salary standards.
To accurately interpret this yearly data, one must understand the significance of the variables involved. For example, when you see \(s(2006) = 95,400\), it means that the average salary for MBA graduates in 2006 was \\(95,400. Similarly, \(s(2009) = 104,000\) indicates the salary increase to \\)104,000 by 2009.
While analyzing such data, consider the following:

• Factors influencing salary changes, like inflation, market demand, or industry shifts.
• Comparisons to previous years to identify growth trends.
• How these figures compare to the cost of obtaining an MBA.

By thoroughly understanding these aspects, students and professionals can make better educational and career choices.