The key phrase in this problem is “grow linearly.”  This tells us that we will use a linear equation (or model).  To create our equation, we need to identify [latex]P_0[/latex] and  [latex]d[/latex].  

First, we need to define how we’re going to measure [latex]n[/latex].   Remember that [latex]P_0[/latex] is the population when [latex]n=0[/latex], so we probably don’t want to literally use the year 0. Since we already know the population in 2003, let us define [latex]n[/latex] to be the number of years since 2003.  Then, [latex]n=0[/latex] corresponds to the year 2003 so that [latex]P_0 = 12,000[/latex].

Next we need to find [latex]d[/latex]. Remember [latex]d[/latex] is the growth per time period, in this case growth per year. Between the two measurements, the population grew by 15,000-12,000 = 3,000, but it took 2007-2003 = 4 years to grow that much. To find the growth per year, we can divide: 3000 elk / 4 years = 750 elk in 1 year.

Alternatively, you can use the slope formula from algebra to determine the common difference, noting that the population is the output of the formula, and time (number of years since 2003) is the input.

[latex]d=\text{ slope }=\frac{\text{change in output}}{\text{change in input}}=\frac{15,000-12,000}{4-0}\frac{\text { elk}}{\text{ year }}=\frac{3000}{4} \frac{\text { elk}}{\text{ year }}=750 \text{ elk per year}[/latex]

We can now write our equation:

[latex]P_n = 12,000 + 750n[/latex]

To answer the question, we need to first note that the year 2014 will be n = 11, since 2014 is 11 years after 2003. Then we plug in 11 for n.

[latex]P_{11}[/latex] = 12,000 + 750(11) = 20,250 elk

Thus, in 2014, this model predicts that there will be 20,250 elk in the national forest.

The value for [latex]P_0[/latex] in this equation is 70, so the initial starting cost is $70. This tells us that there must be an initiation or start-up fee of $70 to join the gym.

The value for [latex]d[/latex] in the equation is 30, so the cost increases by $30 each month. This tells us that the monthly membership fee for the gym is $30 a month.

Since we are given data points, it is a great idea to begin by plotting these data:

Notice that the data appear to have an approximately (not precisely) linear relationship.

While there are many advanced modeling and statistical techniques that can be used to find an equation to model the data, to get an idea of what is happening, we can find an equation by using two pieces of the data – perhaps the data from 1993 and 2003.  [Note: You could have chosen any two years but do note that your answers would likely be a little different from ours–and this is OKAY!  We are creating an imperfect model (an approximating model) for data that are only approximately linear.]

Let’s define [latex]n[/latex] be the number of years since 1993.  Then, [latex]n = 0[/latex] corresponds with 1993 giving [latex]P_0 = 111[/latex] billion gallons and [latex]n=10[/latex] corresponds with 2003.

To find [latex]d[/latex], we need to know how much the gas consumption increased each year, on average. From 1993 to 2003 the gas consumption increased from 111 billion gallons to 133 billion gallons, a total change of 133 – 111 = 22 billion gallons over 10 years. This gives us an average change of 22 billion gallons / 10 year = 2.2 billion gallons per year.

Equivalently,

[latex]d=\text{ slope }=\frac{\text{change in output}}{\text{change in input}}=\frac{133-111}{10-0} \frac{\text{ billion gallons }}{\text{ year }}=\frac{22}{10} \frac{\text{ billion gallons }}{\text{ year }}=2.2[/latex] billion gallons per year.

Thus, gas consumption is changing, on average, by approximately 2.2 billion gallons per year.  We now have enough information to write our equation: [latex]P_n = 111 + 2.2n[/latex]

Calculating values using the explicit form and plotting them with the original data shows how well our model fits the data.  It looks like a pretty good approximation to the given data.  Now, we can use our model to make predictions about the future (or even the past!), assuming that this data trend is consistent before 1992 and after 2004.

To predict the gasoline consumption in 2016, notice that [latex]n = 23[/latex] since 2016 – 1993 = 23 years.

[latex]P_{23} = 111 + 2.2 (23) = 161.6[/latex]

Our model predicts that the US will consume 161.6 billion gallons of gasoline in 2016, if the current trend continues.  This is the answer to our first question.

Next, we need to find when the gasoline consumption will reach 200 billion gallons.  We will start with our model and set the output, [latex]P_n[/latex], equal to 200, and solve then for [latex]n[/latex]:

[latex]P_n = 111 + 2.2n[/latex]                       Set [latex]P_n[/latex] equal to 200

[latex]200 = 111 + 2.2n[/latex]                     Subtract 111 from both sides

[latex]89 = 2.2n[/latex]                                   Divide both sides by 2.2

[latex]40.4545 = n[/latex]

Our model predicts that gasoline consumption will reach 200 billion about 40 years after 1993, which would be in the year 2033.