Problem 34

Question

Finance The median player salary for the Dallas Cowboys was \(\$ 348,000\) in 2004 and \(\$ 555,000\) in 2013 . Write a linear equation giving the median salary \(y\) in terms of the year \(x .\) Then use the equation to predict the median salary in 2019 .

Step-by-Step Solution

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Answer
The linear equation for the median player salary for the Dallas Cowboys with respect to year is \(y = \$23,000x - \$44,892,000\). The predicted salary for the year 2019 is \$601,000.
1Step 1: Calculate the slope
The slope of a line is given by \((y2 - y1)/(x2 - x1)\). Here, \(y1 = \$348,000\) for the year \(x1 = 2004\), and \(y2 = \$555,000\) for the year \(x2 = 2013\). So the slope \(m\) is given by \((\$555,000 - \$348,000) / (2013 - 2004) = \$207,000 / 9 = \$23,000 per year.
2Step 2: Find the y-intercept
The slope-intercept form of a linear equation is \(y = mx + c\), where \(c\) is the y-intercept. We can substitute one of our given points and the determined slope into the equation to solve for \(c\). For simplicity, let's substitute \(x1\) and \(y1\), thus the equation is \$348,000 = $23,000 * 2004 + \(c\) . Solving for \(c\) gives \(c = \$348,000 - \$23,000 * 2004 = -\$44,892,000\).
3Step 3: Write the linear equation
Plugging \(m\) and \(c\) back into our equation, we find \(y = \$23,000x - \$44,892,000\). This is the equation for the median player salary for the Dallas Cowboys as a function of the year.
4Step 4: Predict the median salary in 2019
To predict the median salary in 2019 we substitute \(x = 2019\) in the equation, which gives \(y = \$23,000 * 2019 - \$44,892,000 = \$601,000\). So the predicted median player salary for the Dallas Cowboys in 2019 is \$601,000.

Key Concepts

Slope-Intercept FormMedian Salary PredictionSlope Calculation
Slope-Intercept Form
The slope-intercept form of a linear equation is a way to express any straight line using the formula \( y = mx + c \). Here, \( m \) represents the slope of the line, and \( c \) is the y-intercept, which is where the line crosses the y-axis. This method is particularly useful in identifying the relationship between two variables in a simple, straightforward way.

In our exercise, we used the slope-intercept form to establish a link between the year \( x \), as an independent variable, and the median salary \( y \), as a dependent variable. Once the slope \( m \) and the y-intercept \( c \) were determined, it became easy to express the formula that links the two factors over time.
  • \( m \) accounts for the rate of change in salary over time.
  • \( c \) adjusts the equation to accommodate the starting value when \( x = 0 \).
Understanding this form allows us to quickly draft linear equations whenever initial data such as points on a line are available.
Median Salary Prediction
By employing the linear equation obtained from the slope-intercept form, we can make predictions about the future median salaries. Once the equation \( y = 23000x - 44892000 \) is established, it becomes a powerful tool for forecasting.

To predict the median salary in any given year, simply replace \( x \) with the desired year and solve for \( y \). This exercise predicted the median salary in 2019 using this method:
  • Plug 2019 into the equation: \( y = 23000 \times 2019 - 44892000 \).
  • Carry out the math to find \( y = 601,000 \).
Therefore, the model predicts a median salary of $601,000 for that year. This highlights the benefit of such an equation—it facilitates efficient future predictions based on past data.

However, remember that this is a simplified model and real-world factors may cause actual values to deviate.
Slope Calculation
The slope of a line is a crucial component in understanding and calculating linear equations. Slope describes the steepness of the line, or how much \( y \) changes with a change in \( x \).

In our exercise, the slope \( m \) was found using the formula \( m = \frac{y_2 - y_1}{x_2 - x_1} \). With provided points for the years 2004 and 2013:
  • Starting point: 2004, salary \(348,000.
  • Ending point: 2013, salary \)555,000.
The difference between salaries and years was calculated:

\( m = \frac{555,000 - 348,000}{2013 - 2004} = \frac{207,000}{9} = 23,000 \).

This means, on average, the salary increased by $23,000 each year, which is the rate of change or the slope of the line. This slope is integral to predicting future values within the constructed linear model.