Thursday, April 25, 2013

Differentiation Rules - The Power Rule


Welcome to my second post of my series on differentiation formulas.  So far in my recent posts, I have explored in depth all about the concept of derivatives and using differentiation to find them, and then I started this current series with an easy theorem to remember for finding the derivative of a constant function.  This follow-up post will now explain to you probably one of the most used methods for finding derivatives: The Power Rule for Differentiation.

First, allow me to present this rule to you in all it's nasty-looking glory:


The only condition that this rule has is that n must be a positive integer.  OK, maybe it's not as frightening as I would have made it out to be... but it still doesn't look very friendly.  However, when you start doing questions with it, you will understand the easy workflow process and find that it actually is very simple to remember and use!

Let's try a few.  Trust me, you will see the pattern and rhythm right away!

Find the derivative of f(x) = x3.

According to our rule, the n value here is 3, so then we have:

f'(x) = 3x3-1 = 3x2.

That's all there is to it!

Next, differentiate the function f(x) = x500.

This one seems a bit tougher, but you do the exact same thing.  Check it out:

f'(x) = 500x500-1 = 500x499

Easy, right?

Here's a simple image that can help you to remember what you have to do.  Like I said, once you start doing these, it is very hard to mess up.

Here's a better way to remember this, which may be even easier to think of but harder to draw:  imagine that the exponent is a pile of sequential number cards (e.g. 1, 2, 3, 4, 5...) with the high card on top.  When you have to find the derivative, take the top card and place it in front of the x, leaving the card beneath it (which is 1 number smaller) visible.  It's easier to actually do this than to draw it, but it's a great way to remember this formula because it is so easy to visualize!

For fun (!), you can see the proof of this by using the Binomial Theorem.  I won't go into that much detail here, but in a nutshell, if you start with the definition of a derivative and let f(x) = xn, you can expand the (x + h)n term with the Binomial Theorem.  Since the definition requires you to find the limit as h approaches zero, this causes all but the first term of the expansion to equal zero, leaving you with the above result.

So, now you know one of the most common differentiation rules.  In my next posts, I'll show you how to apply this rule to your terms when you have a constant value in front of the x.  It's as easy as to do as this one!  Thanks for reading, and make sure you click the Like and +1 buttons on this page!


Tuesday, April 23, 2013

Differentiation Rules - Derivative of a Constant Function


For those of you just tuning in, my last post was a mega-post about derivatives and an introduction to differential calculus.  If you need some help getting started with understanding how to find derivatives, I highly recommend giving that a read.  One impression you may have of this concept is that it requires a lot of work - lots of lengthy formulas and limit calculations.  While you could certainly use those methods for all of your differentiating questions, you would be wasting your time!  In my never-ending quest to show you mathematics made easy, today I am going to start a series of posts about differentiation formulas - essentially, shortcuts through a lot of the repetitive and lengthy work needed if you were to explicitly use the definition of a derivative!  Once you learn some of these tricks, you will fly through your calculus homework!

(To be honest, these aren't so much tricks as they are actual mathematical theorems and rules.)

The first rule should be simple to understand, if you think about it.  Take any constant function, f(x) = c.  The derivative of this, that is, f'(x), will always be zero.


It's an easy one to remember, and the explanation is easy to visualize as well.  If you have a graph of f(x) = c, you basically have a horizontal line that never varies.  For every value of x on your curve, f(x) is the same, constant value.  So, it has no slope - its slope is zero.  Furthermore, if you refer back to my graphical explanation of derivatives in my previous post, you will see that the derivative f'(x) is equal to the slope of f(x).  So, if we have a line that has no slope here, we can see how this rule comes together.  For fun, you can practice your limit notation and long-hand derivative calculations to prove that this is the case.  Refer back again to my last post and use the definition of a derivative.  Hint: in your calculation, f(x) = c, and f(x + h) = c.

This is one of the easiest differentiation formulas (if you can call it that) that you are going to encounter, so memorize this one, and get ready for something a little bit more challenging in my next post: the Power Rule.

Please remember to click the Like and +1 buttons on this page if it was helpful!  I really appreciate it!


Thursday, April 11, 2013

Derivatives and an Introduction to Differential Calculus


One of the main concepts studied in the field of differential calculus is based on the notion of change - specifically, how one quantity changes compared to another.  Perhaps a more succinct version of this physical definition would be "rate of change."  Alternately, a geometric definition could simply be the slope of a curve at a particular point.  The underlying key to this branch of mathematics is the concept of the derivative.  In this post, I will introduce various aspects of the derivative - please click the Facebook Like button if this is helpful for you!

First, let's consider the derivative in terms of rate of change.  And to do that, let's talk about velocity.  We know that velocity is equal to distance per unit time.  However, that is a very general description of it.  If you drive from your home to the grocery store on the other side of town, you can do the math by dividing your total distance travelled by the time it took you to get there, but what does this number tell you?  It actually tells you the average velocity of your trip.  Think about it.  You had to stop for red lights, stop signs, pedestrians.  Maybe you sped up to pass a slow driver.  Don't forget about the actual acceleration of your car from a standstill, and then the deceleration whenever you needed to stop.  All of this factors into the calculation of your average velocity, which is simply how far you go in a measured amount of time.

Now, let's consider how to calculate the average velocity of your car between your home and the first stop sign, while on your way to the grocery store.  You no longer have several stops to deal with.  You get in your car, accelerate, then as you approach the stop sign, you decelerate to a stop.  Your average velocity is calculated from a much shorter interval, and will have much less variation to it.  So, your average velocity will be more representative of your actual velocity at any given time.

To extend this demonstration even further, let's consider the small portion of your trip that is measured between two street lights 10 meters apart that you pass while you are in full motion.  That is, let's assume that we want to measure the velocity without having to calculate stop signs, etc.  Our time interval for the measurement is much smaller, and calculating the velocity by dividing the distance by the time it takes to go from one light to the other is even more representative of your velocity at any point between them.

What I am trying to demonstrate is the concept of instantaneous velocity.  If you shrink down your time interval of measurement infinitesimally, the two time points approach each other at a single point, and so the average velocity between the two super close points approaches the instantaneous velocity of the single point.

Graphically, this is essentially the same thing you do when you calculate the slope of a tangent line to a curve.  You pick two lines on the curve and calculate the slope of the line between them, and then you use limits to make the points get closer and closer together until they are almost the same, and the slope of the line connecting those two infinitely close points is the tangent.  (Check out my previous post about using limits to find tangents if you'd like a refresher of this topic: http://sk19math.blogspot.ca/2012/06/using-limits-to-find-tangents.html)

Because this type of limit occurs so frequently in maths, science, and engineering, it is given the special name of "derivative," and you calculate derivatives through the process of "differentiation."  So, one interpretation of the derivative is an expression of the instantaneous rate of change (velocity) at a particular point on the curve - a large derivative corresponds to a high rate of change (a steep curve), and conversely a small derivative corresponds to a low rate of change (a relatively flat curve).  As a specific example, if you actually have a graph of position (displacement) of an object vs. time, the derivative of the curve at any time point represents the velocity of that object at that specific time.  This may take a little practice to become comfortable with the concept, but suffice it to say at this point that learning how to use derivatives is incredibly important to be able to work out more complex concepts relatively easily.

Let's look at this now in the more formal terms of mathematical symbols and equations.  Consider any curve y = f(x).


Now, let us identify the point P on the curve f(x) for when x = a.  That is to say, the point (a, f(a)).

Now, let's go a step further, and identify a point Q that is h units away from a on the x-axis.  If it is h units away from a, we can call it "a + h".  (If this is confusing, think about it with numbers instead.  Start at, say, x = 3 (instead of a).  Now we want to know what is going on 5 units (instead of h) away from x = 3.  In other words, we have 3, and we have 3 + 5.)  As such, we can therefore identify a point Q ((a + h), f(a + h)).


Now that we have two arbitrary points, let's determine the slope of the straight line that would connect the two.  We can use the same slope formula that we always use, slope = rise/run, but substitute in our variables that we identified above.  So, we have:




Now, imagine that the distance h between the two points is getting smaller and smaller.  Or in other words, consider the case of when h approaches 0.  By doing this, we calculate the slope of the line connecting two infinitesimally close points - which means that we are actually approaching the slope of the tangent line to the curve at point a.  In this case, we would express this slope as a limit in the following way, which actually corresponds to the definition of the derivative of a function f at a number a.  The derivative is given the special symbol f'(x), and we say "f prime x", and we express it like this:


Another way of expressing this can be found if we recognize that a + h is really just any x value.  So, we can say x = a + h (and by extension, h = x - a), and modify the above derivative definition accordingly:


With this modified equation, it actually becomes a matter of arithmetic to determine the slope at a point. Here is an example of a kind of question that you will see:

"Find the derivative (or, determine the slope of the tangent) of the function f(x) = x2 - 4 at a number a."

To do this, write the provided equation into the definition, and reduce until you have an answer.  Notice below how I combine terms and recognize the identity of a difference of squares.








What this final result tells you is that for our curve, f(x) = x2 - 4, at any number a along it, the slope of the tangent (AKA, the derivative at that point) is equal to the term 2a.  Graph it out and try with several values to convince yourself that it's true!  Consider when x = 5.  You can determine from the original equation that we have the point (5, 21).  At this point on the curve, the slope of the tangent equals 2 x 5 = 10.


Going back to the definition of the derivative that I gave above, you can also apply the concept of point-slope form to it to get a different way of seeing it.  Letting y = f(x), you can rearrange the definition as follows, by simple reorganization of the terms:


Here's a more visual exercise that you may soon encounter.

"If you are provided a graph of a function f(x) - not necessarily the equation - sketch out what the graph of the derivative f'(x) would look like."

When you actually have the numbers and equation, this becomes much easier... assuming you know how to easily recognize derivatives from the original equations.  However, if provided ONLY the picture of the curve, this becomes a bit more abstract, but not really that challenging.  It DOES require you to understand the concept of derivatives and rate of change though.  Here is why.  Take some random curve that you can draw.  Any curve will do for this exercise:


The key is rate of change.  We have seen that slope is equal to rate of change, so we want to pay particular attention to the slope at several points.  And the easiest points to notice are those where the slope is equal to 0.  These are all the peaks and valleys of the curve.  What I have done next is highlight with red bars all of the zero-slopes:


Now, to proceed with sketching the graph of the derivative f'(x) vs x, you can start by plotting the points where f'(x) is equal to zero.  From there you can then go on to say where the curve of f(x) has a positive, increasing slope, and then sketch that into your f'(x) graph accordingly.  Similarly, decreasing slopes on the f(x) curve will be negative values on the f'(x) curve.  For the sake of this exercise, don't worry so much about how high or low the slopes are.  Just focus on whether they are positive or negative at the various parts of the graph.  I have gone ahead and plotted out the actual curve of the derivative below in green, alongside the original curve of f(x).  You can see that the f'(x) curve crosses zero wherever the curve for f(x) has peaks or valleys, and the steeper the f(x) curve, the more extreme the f'(x) curve at that same point.


Of course, having a mathematical definition wouldn't be any fun if there were no conditions or rules associated with it - and the definition of derivatives is no exception.  One such rule states that a function is differentiable at a point a if the derivative f'(a) exists.  Seems intuitive enough.  If a derivative at a point exists, then the base function is differentiable at that point.  Probably one of those rules that doesn't really even need to be said.  :)

I'm not going to graph this one out, but it is for you to think on.  Consider the case of f(x) = |x|.  Where is it differentiable?

If you consider the derivative of the left hand side, it equals -1.  The f'(x) on the right hand side is 1.  This function then is obviously differentiable when x < 0, and when x > 0.  But what about when x = 0?  In this case, since the right hand limit approaches 1 as x approaches 0 from the right, and the left hand limit approaches -1 as x approaches 0 from the left, one must conclude that f'(0) does not exist because both of the one-sided limits approach different numbers.

An extension of this example actually describes a second rule for limits: if f'(a) exists, then the function f(x) is continuous as a.  Recall that continuity of a curve is based on the notion that as you approach a point from both the left and the right, the limit of each side approaches the same value.  In the example above, approaching 0 from either side resulted in different limits, and hence the graph is not continuous at 0.

Keep this in mind as you see various graphs of functions.  Curves that have a sharp point will not be differentiable at the point, for the reason given above.  Similarly, discontinuous curves (i.e. curves with gaps in them) will not have a derivative at the break point either because the one-sided limits do not agree.  If f(x) is not continuous as point a, then f'(a) does not exist.  A third condition to watch out for is where a graph has a vertical tangent line, in which case the slope is infinite.

Now, I'm going to wrap up this mammoth of a maths post with something a bit easier to talk about: notation of derivatives.  I have already described a few ways to express these values.  I talked about expressing them as limits, and using infinitesimally smaller intervals, and that is a good way to work through them.  Symbolically, I said that you can write f'(x) to denote the derivative of the functions f(x).  This will likely be the easiest way for you to use it and to recognize it, though here are a few others that mean the same thing:


Each of these terms means the exact same thing.  In particular, the D and d/dx are specifically called the differentiation operators, and you can see they have a few variations.  Similarly, dy/dx is symbolic of derivatives for historical reasons.  Read up on Gottfried Wilhelm Leibniz to learn more about the origins of calculus, where you will see that he introduced this way of representing it.  Sometimes, you may see dy/dx referred to as "Leibniz notation."

And with that final tidbit of mathematical goodness, I am going to end this post.  I intend to follow this with another post in the near future that introduces differentiation methods and strategies.  Much like the exponent rules, there are also several differentiation rules, and I hope to be able to explain them for you as well.  If you have made it to this point of my post, thanks for reading, and please be sure to click the Facebook Like button below or at the top, and I'd appreciate a Google +1 as well below if this was helpful!


Saturday, March 30, 2013

Top 5 Most Popular Posts of March


I think I'm starting to see a trend with my posts!

The top 5 most popular posts of March are a slightly shuffled variation of my top posts from February (you can take a look at those results here: http://sk19math.blogspot.com/2013/03/popular-posts-february-2013.html).

There was a chance that one of those 5 might have dipped and allowed a new star post to rise, but alas, this was not the month for that to happen.  However, to keep things interesting, the order of these leaders this time around is slightly different, so at least I can say that there is a degree of variation here.  So, the anticipation is finally over, and I present to you my most popular stories of March:
  1. Stretching and Compressing Graphs.  It was number 2 last month, but it takes the crown for March!  This post discusses all you need to know to be able to stretch and squash your graphs.

  2. Converting Point-Slope Form to Standard Form.  Slipped out of the top spot for a month, but could very easily win it back in April!  Learning how to convert between point-slope form and standard form is crucial, and this post details what you need to know!  (I have also recently added a companion post about Point Slope Form, which can be found here: http://sk19math.blogspot.com/2013/02/point-slope-form.html).

  3. Trigonometry - Secant, Cosecant, Cotangent.  The lesser known, yet equally useful, trig functions.  They are slight variations of sine, cosine, and tangent, so if you know those 3, you don't have far to go to understand these ones - though this post helps you figure it all out.

  4. Special Angles in Trigonometry.  Special angles are easy to remember, and are extraordinarily helpful in getting your trig work done quickly.  Learn the triangles that I explain in my post, and you'll be all set!

  5. Which Measure of Central Tendency to Use? Mode, Mean, or Median?  With several different ways of representing the "center" of a data set, it is frequently asked which is the best way.  In this post, you can learn some general guidelines to help you select the best way of representing your data.
To find more great explanations and discussions of math concepts on my site, browse the Math Concepts Explained table of contents.  Alternately, you can enter your topic of interest in the search bar at the top of every page.

If you enjoy Math Concepts Explained, I invite you to join the many other students, teachers, and math enthusiasts who follow my site:
Thanks to all of my visitors for your support!


Wednesday, March 27, 2013

The Midsegment Theorem


A while ago, I posted a very popular post that explained how to calculate the midpoint of a line. A lot of people have viewed that page, and so I thought that this somewhat related story might also be equally as interesting for my viewers.

Guillermo, over at "Proofs from the Book," has recently posted an interesting discussion about the Midsegment Theorem, which deals with the line that connects the two midpoints of two sides of a triangle. This concept is useful when doing proofs with triangles. According to Guillermo, the midsegmet (or midline) has these properties:

"(1) the midsegment connecting the midpoints of the two sides of a triangle is parallel to the third side and (2) its length is also half of the third side." 

If you learn how to recognize this geometrical identity, it will be very valuable to you when working with geometry or trigonometry questions. The post at that site goes into great detail to explain each step of the proof for each of these two theorems. It is fascinating to see the in-depth steps that go towards demonstrating a mathematical proof, so I recommend this page at http://proofsfromthebook.com/2013/03/25/the-midsegment-theorem/ for anyone interested in learning more about the midsegment theorem.


Related Posts