On this screen we’re going to determine the limit of a function at a point where the function is undefined, using an approach that’s very similar to that of Lab 1.1 at the end of Chapter 1.
We don’t know (or pretend to not know, anyway) the exact height, the y-value, of that hole. But we have marked its approximate location on the y-axis with the label “L” since the (currently unknown) exact value of this y-location is
\[\lim_{x \to 1}\overbrace{\frac{2^x\, -\, 2}{x-1}}^{g(x)} = L\]
We’re going to work to determine the value of L to whatever level of precision we would like. While this may seem like a silly exercise, we’re actually engaging in a deep use of Calculus tools (as well as some basic ideas of data analysis).
Note: If you have taken Calculus before, you may know a fancy way to compute this limit exactly. You could also use any number of online resources to find its value easily. Please don’t. Later in the course we’re going to need the crucial ideas and tools we’re developing here, and so ask that you complete the activities below. In the end we don’t really care what “the answer” is to this particular limit; instead, we care that you’re working to develop an understanding of Calculus and its tools that will allow you to do whatever you want in the future.
The interactive Desmos graph below shows the graph of our function of interest, $g(x)=\dfrac{2^x – 2}{x-1}.$ As we start to estimate the limit of the function at $x=1,$ let’s first develop an upper bound by finding an overestimate using a point with x-value greater than $x=1.$ You can move the point on the curve to wherever you’d like, as long as $x_1 > 1.$
Your current upper bound for the value of L is
L ≤ g(1.76) = 1.8249753
When you’re ready, click the button to add this first value to your data table. It doesn’t matter where exactly the point is, or how close it is to $x=1$; we just need a first value to get started here.
Data Point | $x_1$ | $d = x_1 – 1$ | L Upper Bound Value: $g(x_1)$ |
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Great! Since this first value (with $x_1 > 1$) is an overestimate of L, we have thus far determined that
\[ L \le 1.8249753|\]
Notice that we have added the horizontal distance d = to the table, which is the distance from $x=1$ to your point $x_1$: $x_1 = 1+d$. This horizontal distance is also now visible on the graph above so you can see it. (You may need to zoom in for it to be visible.)
You’ll add more data as we continue. Please proceed to Part II.
Rather than having you place a point with x-value $x_2 \le 1,$ we’ve instead used the value of d you set in Part I to be an equal distance to the left of $x = 1$: $x_2 = 1 – d.$
Specifically, you set $d = \text{[you have not yet clicked the “Add data” button in Part I]}$ and so
$x_2 =\, $ ?
and we see in the graph below that your current lower bound for the value of L is
$g(x_2) = $[You have not yet clicked the “Add data” button in Part I] $\le L$
Data Points | $d$ | $x_2 = 1 – d$ | L Lower Bound Value: $g(x_2)$ | $x_1 = 1 + d$ | L Upper Bound Value: $g(x_1)$ |
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3. The difference between your current overestimate and underestimate values is your “error bound.” What is its current value?
The current size of your error bound, the difference between the upper and lower bounds, is
\[\]
Our fourth and final Approximation Question is
4. How can the error be made smaller than any predetermined bound?
You probably have an answer to this question in mind. You’ll get to implement it in Part III below.
As happened on the preceding screens, it can become tough to see what’s going on with both the function’s output and input as we zoom in more and more. Hence, as we did earlier, we’re going to split the graph into two linked graphs, the top one focusing on the function’s output and the lower one focusing on the function’s input.
We’ve set the initial size of d set to the value we used in Parts I and II above. You can see visually the values for $g(x_1)$ and $g(x_2)$ bounding the value of L.
Below the graphs is a slider that allows you to set the value of d. Give it a try to see how it works.
Data Points | $d$ | $x_2 = 1 – d$ | L Lower Bound Value: $g(x_2)$ | $x_1 = 1 + d$ | L Upper Bound Value: $g(x_1)$ | Error bound: $g(x_2) – g(x_1)$ |
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target error bound < ?
Please adjust the size of d and generate another set of data. You will then see whether your new range for the value of L is sufficiently small, or if you need to make d smaller still. You can make these adjustments as many times as you’d like.
To complete the lab, let’s make our estimate of L better still.
In particular, we are now setting your target tolerance level to be
error bound < 0.001. That means you will determine the value of L to within ± 0.001, three decimal places.
Reminder: To change only the graph’s vertical scale on desktop or laptop, place your cursor near the left edge of the calculator and hold the SHIFT key while you scroll.
Data Points | $d$ | $x_2 = 1 – d$ | L Lower Bound Value: $g(x_2)$ | $x_1 = 1 + d$ | L Upper Bound Value: $g(x_1)$ | Error bound: $g(x_2) – g(x_1)$ |
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Now that you have found a value of d that gives an error bound < 0.001, what is the largest value of d to within four decimal places that meets this criterion?
The largest value of d that still gives an error bound of 0.001 is:
We think it is important that you have the experience, once, of “zeroing in” on a limit by generating the function’s output values for input values of x that are increasingly close to $x=a,$ in order to gain an even better understanding of what “the limit” really represents. While we won’t ask you to repeat the detail of this process for other functions, we hope that you will keep the Approximation Framework in mind and imagine applying it to the functions on the next screen — where we will look at some situations where the limit does, and does not, exist.
For now, if you have any questions or comments about anything on this screen, or about limits in general, please do our Learning Community a favor and post them on the Forum!
Reference for Oehrtman’s Five Questions:
Oehrtman, M. (2008). Layers of abstraction: Theory and design for the instruction of limit concepts. In M. P. Carlson & C. Rasmussen (Eds.), Making the Connection: Research and Teaching in Undergraduate Mathematics Education, (MAA Notes, Vol. 73, pp. 65-80). Washington, DC: Mathematical Association of America.
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