# Entries Categorized as 'Mathematics'

#### December 14, 2017

One of my students is taking an advanced statistics course–mostly online–and it introduced her to the statistical package R. I’ve been meaning to learn how to use R for a while, so I had her show me how use it. This allowed me to give her a final exam that used some PEW survey data for analysis. (I used the data for the 2013 LGBT survey). These are my notes on getting the PEW data, which is in SPSS format, into R.

## Instructions on Getting PEW data into R

Go to the link for the 2013 LGBT survey“>2013 LGBT survey and download the data (you will have to set up an account if you have not used their website before).

- There should be two files.
- The .sav file contains the data (in SPSS format)
- The .docx file contains the metadata (what is metadata?).

- Load the data into R.
- To load this data type you will need to let R know that you are importing a foreign data type, so execute the command:

> library(foreign)

To get the file’s name and path execute the command:
> file.choose()

The file.choose() command will give you a long string for the file’s path and name: it should look something like “C:\\Users\…” Copy the name and put it in the following command to read the file (Note 1: I’m naming the data “dataset” but you can call it anything you like; Note 2: The string will look different based on which operating system you use. The one you see below is for Windows):
> dataset = read.spss(“C:\\Users\...”)

To see what’s in the dataset you can use the summary command:
> summary(dataset)

To draw a histogram of the data in column “Q39” (which is the age at which the survey respondents realized they were LGBT) use:
> hist(dataset$Q35)

If you would like to export the column of data labeled “Q39” as a comma delimited file (named “helloQ39Data.csv”) to get it into Excel, use:
> write.csv(dataset$Q39, ”helloQ39Data.csv”)

This should be enough to get started with R. One problem we encountered was that the R version on Windows was able to produce the histogram of the dataset, while the Mac version was not. I have not had time to look into why, but my guess is that the Windows version is able to screen out the non-numeric values in the dataset while the Mac version is not. But that’s just a guess.

Histogram showing the age at which LGBT respondents first felt that they might be something other than heterosexual.

**Citing this post**: **Urbano**, L., 2017. Getting Data into R, Retrieved February 26th, 2018, from *Montessori Muddle*: http://MontessoriMuddle.org/ .

**Attribution (Curator's Code )**: Via: ᔥ Montessori Muddle; Hat tip: ↬ Montessori Muddle.

Posted in MathematicsNo Comments » - Tags: R, statistics

#### March 1, 2017

Chessboard under regular (day) light.

I used the computer controlled (CNC) Shopbot machine at the Techshop to drill out 64 square pockets in the shape of a chessboard. One of my students (Kathryn) designed and printed the pieces as part of an extra credit project for her Geometry class.

The pockets were then filled with a clear eqoxy to give a liquid effect. However, I mixed in two colors of pigmented powder to make the checkerboard. The powder was uv reactive so it fluoresces under black (ultra-violet) light.

Under a black (ultra violet) light bulb.

The powder also glows in the dark.

Glowing in the dark.

**Citing this post**: **Urbano**, L., 2017. Liquid Chessboard, Retrieved February 26th, 2018, from *Montessori Muddle*: http://MontessoriMuddle.org/ .

**Attribution (Curator's Code )**: Via: ᔥ Montessori Muddle; Hat tip: ↬ Montessori Muddle.

Posted in 3d Print, Mathematics, TechShopNo Comments » - Tags: Art, geometry, TechShop

#### January 31, 2017

This little embeddable, interactive app uses *n*th order polynomials to approximate a few curves to demonstrate the Taylor Series.

**Citing this post**: **Urbano**, L., 2017. Demonstrating Taylor Series Approximations with Graphs, Retrieved February 26th, 2018, from *Montessori Muddle*: http://MontessoriMuddle.org/ .

**Attribution (Curator's Code )**: Via: ᔥ Montessori Muddle; Hat tip: ↬ Montessori Muddle.

Posted in Calculus, MathematicsNo Comments » - Tags: calculus, embedable apps, embeddable graphs, graphing

#### December 18, 2016

One of the middle schoolers built a potato gun for his math class. He was looking a the mathematical relationship between the amount of fuel (hair spray) and the hang-time of the potato. To augment this work, I had my Numerical Methods class do the math and create analytical and numerical models of the projectile motion.

One of the things my students had to figure out was what angle would give the maximum range of the projectile? You can figure this out analytically by finding the function for how the horizontal distance (x) changes as the angle (theta) changes (i.e. x(theta)) and then finding the maximum of the function.

Initial velocity vector (v) and its component vectors in the x and y directions for a given angle.

## Distance as a function of the angle

In a nutshell, to find the distance traveled by the potato we break its initial velocity into its x and y components (v_{x} and v_{y}), use the y component to find the flight time of the projectile (t_{f}), and then use the v_{x} component to find the distance traveled over the flight time.

Starting with the diagram above we can separate the initial velocity of the potato into its two components using basic trigonometry:

,

so,

,

Now we know that the height of a projectile (y) is given by the function:

(you can figure this out by assuming that the acceleration due to gravity (a) is constant and acceleration is the second differential of position with respect to time.)

To find the flight time we assume we’re starting with an initial height of zero (y_{0} = 0), and that the flight ends when the potato hits the ground which is also at zero ((y_{t} = 0), so:

Factoring out *t* gives:

Looking at the two factors, we can now see that there are two solutions to this problem, which should not be too much of a surprise since the height equation is parabolic (a second order polynomial). The solutions are when:

The first solution is obviously the initial launch time, while the second is going to be the flight time (t_{f}).

You might think it’s odd to have a negative in the equation, but remember, the acceleration is negative so it’ll cancel out.

Now since we’re working with the y component of the velocity vector, the initial velocity in this equation (v_{0}) is really just v_{y}:

so we can substitute in the trig function for v_{y} to get:

Our horizontal distance is simply given by the velocity in the x direction (v_{x}) times the flight time:

which becomes:

and substituting in the trig function for v_{x} (just to make things look more complicated):

and factoring out some of the constants gives:

Now we have distance as a function of the launch angle.

We can simplify this a little by using the double-angle formula:

to get:

## Finding the maximum distance

How do we find the maxima for this function. Sketching the curve should be easy enough, but because we know a little calculus we know that the maximum will occur when the first differential is equal to zero. So we differentiate with respect to the angle to get:

and set the differential equal to zero:

and solve to get:

Since we remember that the arccosine of 0 is 90 degrees:

And thus we’ve found the angle that gives the maximum launch distance for a potato gun.

**Citing this post**: **Urbano**, L., 2016. Maximum Range of a Potato Gun, Retrieved February 26th, 2018, from *Montessori Muddle*: http://MontessoriMuddle.org/ .

**Attribution (Curator's Code )**: Via: ᔥ Montessori Muddle; Hat tip: ↬ Montessori Muddle.

Posted in Mathematics, ProgrammingNo Comments » - Tags: math, math and programming, numerical methods, physics, projectile motion

#### November 18, 2016

Tyler Vigen has a great website Spurious Correlations that shows graphs of exactly that.

A spurious correlation.

Great for explaining what correlation means, and why correlation does not necessarily mean causation.

**Citing this post**: **Urbano**, L., 2016. Spurious Correlations, Retrieved February 26th, 2018, from *Montessori Muddle*: http://MontessoriMuddle.org/ .

**Attribution (Curator's Code )**: Via: ᔥ Montessori Muddle; Hat tip: ↬ Montessori Muddle.

Posted in MathematicsNo Comments » - Tags: curious, statistics

#### November 3, 2016

Previously, I showed how to solve a simple problem of motion at a constant velocity analytically and numerically. Because of the nature of the problem both solutions gave the same result. Now we’ll try a constant acceleration problem which should highlight some of the key differences between the two approaches, particularly the tradeoffs you must make when using numerical approaches.

**The Problem**

- A ball starts at the origin and moves horizontally with an acceleration of 0.2 m/s
^{2}. Print out a table of the ball’s position (in x) with time (every second) for the first 20 seconds.

**Analytical Solution**

We know that acceleration (*a*) is the change in velocity with time (*t*):

so if we integrate acceleration we can find the velocity. Then, as we saw before, velocity (*v*) is the change in position with time:

which can be integrated to find the position (x) as a function of time.

So, to summarize, to find position as a function of time given only an acceleration, we need to integrate twice: first to get velocity then to get x.

For this problem where the acceleration is a constant 0.2 m/s^{2} we start with acceleration:

which integrates to give the *general solution*,

To find the constant of integration we refer to the original question which does not say anything about velocity, so we assume that the initial velocity was 0: i.e.:

at *t = 0* we have *v = 0*;

which we can substitute into the velocity equation to find that, for this problem, c is zero:

making the *specific* velocity equation:

we replace *v* with *dx/dt* and integrate:

This constant of integration can be found since we know that the ball starts at the origin so

at *t = 0* we have *x = 0*, so;

Therefore our final equation for x is:

### Summarizing the Analytical

**To summarize** the analytical solution:

These are all a function of time so it might be more proper to write them as:

Velocity and acceleration represent rates of change which so we could also write these equations as:

or we could even write acceleration as the second differential of the position:

or, if we preferred, we could even write it in prime notation for the differentials:

## The Numerical Solution

##

As we saw before we can determine the position of a moving object if we know its old position (*x*_{old}) and how much that position has changed (*dx*).

where the change in position is determined from the fact that velocity (*v*) is the change in position with time (*dx/dt*):

which rearranges to:

So to find the new position of an object across a timestep we need two equations:

In this problem we don’t yet have the velocity because it changes with time, but we could use the exact same logic to find velocity since acceleration (*a*) is the change in velocity with time (*dv/dt*):

which rearranges to:

and knowing the change in velocity (dv) we can find the velocity using:

Therefore, we have four equations to find the position of an accelerating object (note that in the third equation I’ve replaced *v * with *v*_{new} which is calculated in the second equation):

These we can plug into a python program just so:

*motion-01-both.py*

from visual import *
# Initialize
x = 0.0
v = 0.0
a = 0.2
dt = 1.0
# Time loop
for t in arange(dt, 20+dt, dt):
# Analytical solution
x_a = 0.1 * t**2
# Numerical solution
dv = a * dt
v = v + dv
dx = v * dt
x = x + dx
# Output
print t, x_a, x

which give output of:

>>>
1.0 0.1 0.2
2.0 0.4 0.6
3.0 0.9 1.2
4.0 1.6 2.0
5.0 2.5 3.0
6.0 3.6 4.2
7.0 4.9 5.6
8.0 6.4 7.2
9.0 8.1 9.0
10.0 10.0 11.0
11.0 12.1 13.2
12.0 14.4 15.6
13.0 16.9 18.2
14.0 19.6 21.0
15.0 22.5 24.0
16.0 25.6 27.2
17.0 28.9 30.6
18.0 32.4 34.2
19.0 36.1 38.0
20.0 40.0 42.0

Here, unlike the case with constant velocity, the two methods give slightly different results. The analytical solution is the correct one, so we’ll use it for reference. The numerical solution is off because it does not fully account for the continuous nature of the acceleration: we update the velocity ever timestep (every 1 second), so the velocity changes in chunks.

To get a better result we can reduce the timestep. Using *dt = 0.1* gives final results of:

18.8 35.344 35.532
18.9 35.721 35.91
19.0 36.1 36.29
19.1 36.481 36.672
19.2 36.864 37.056
19.3 37.249 37.442
19.4 37.636 37.83
19.5 38.025 38.22
19.6 38.416 38.612
19.7 38.809 39.006
19.8 39.204 39.402
19.9 39.601 39.8
20.0 40.0 40.2

which is much closer, but requires a bit more runtime on the computer. And this is the **key tradeoff** with numerical solutions: *greater accuracy requires smaller timesteps which results in longer runtimes on the computer*.

### Post Script

To generate a graph of the data use the code:

from visual import *
from visual.graph import *
# Initialize
x = 0.0
v = 0.0
a = 0.2
dt = 1.0
analyticCurve = gcurve(color=color.red)
numericCurve = gcurve(color=color.yellow)
# Time loop
for t in arange(dt, 20+dt, dt):
# Analytical solution
x_a = 0.1 * t**2
# Numerical solution
dv = a * dt
v = v + dv
dx = v * dt
x = x + dx
# Output
print t, x_a, x
analyticCurve.plot(pos=(t, x_a))
numericCurve.plot(pos=(t,x))

which gives:

Comparison of numerical and analytical solutions using a timestep (dt) of 1.0 seconds.

**Citing this post**: **Urbano**, L., 2016. Numerical and Analytical Solutions 2: Constant Acceleration, Retrieved February 26th, 2018, from *Montessori Muddle*: http://MontessoriMuddle.org/ .

**Attribution (Curator's Code )**: Via: ᔥ Montessori Muddle; Hat tip: ↬ Montessori Muddle.

Posted in Calculus, Mathematics, ProgrammingNo Comments » - Tags: motion, numerical methods, physics, physics and calculus, programming

#### November 3, 2016

We’ve started working on the physics of motion in my programming class, and really it boils down to solving differential equations using numerical methods. Since the class has a calculus co-requisite I thought a good way to approach teaching this would be to first have the solve the basic equations for motion (velocity and acceleration) analytically–using calculus–before we took the numerical approach.

## Constant velocity

**Question 1**. A ball starts at the origin and moves horizontally at a speed of 0.5 m/s. Print out a table of the ball’s position (in *x*) with time (*t*) (every second) for the first 20 seconds.

**Analytical Solution:**

Well, we know that *speed * is the change in position (in the x direction in this case) with time, so a constant velocity of 0.5 m/s can be written as the **differential equation**:

To get the ball’s position at a given time we need to integrate this differential equation. It turns out that my calculus students had not gotten to integration yet. So I gave them the 5 minute version, which they were able to pick up pretty quickly since integration’s just the reverse of differentiation, and we were able to move on.

Integrating gives:

which includes a constant of integration (*c*). This is the **general solution** to the differential equation. It’s called the general solution because we still can’t use it since we don’t know what *c* is. We need to find the **specific solution** for this particular problem.

In order to find *c * we need to know the actual position of the ball is at one point in time. Fortunately, the problem states that the ball starts at the origin where *x=0* so we know that:

So we plug these values into the general solution to get:

solving for *c* gives:

Therefore our *specific solution* is simply:

And we can write a simple python program to print out the position of the ball every second for 20 seconds:

*motion-01-analytic.py*

for t in range(21):
x = 0.5 * t
print t, x

which gives the result:

>>>
0 0.0
1 0.5
2 1.0
3 1.5
4 2.0
5 2.5
6 3.0
7 3.5
8 4.0
9 4.5
10 5.0
11 5.5
12 6.0
13 6.5
14 7.0
15 7.5
16 8.0
17 8.5
18 9.0
19 9.5
20 10.0

**Numerical Solution:**

Finding the numerical solution to the differential equation involves not integrating, which is particularly good if the differential equation can’t be integrated.

We start with the same differential equation for velocity:

but instead of trying to solve it we’ll just approximate a solution by recognizing that we use *dx/dy* to represent when the change in x and t are really, really small. If we were to assume they weren’t infinitesimally small we would rewrite the equations using deltas instead of d’s:

now we can manipulate this equation using algebra to show that:

so the change in the position at any given moment is just the velocity (0.5 m/s) times the **timestep**. Therefore, to keep track of the position of the ball we need to just add the change in position to the old position of the ball:

Now we can write a program to calculate the position of the ball using this numerical approximation.

*motion-01-numeric.py*

from visual import *
# Initialize
x = 0.0
dt = 1.0
# Time loop
for t in arange(dt, 21, dt):
v = 0.5
dx = v * dt
x = x + dx
print t, x

I’m sure you’ve noticed a couple inefficiencies in this program. Primarily, that the velocity *v*, which is a constant, is set inside the loop, which just means it’s reset to the same value every time the loop loops. However, I’m putting it in there because when we get working on acceleration the velocity will change with time.

I also import the visual library (vpython.org) because it imports the numpy library and we’ll be creating and moving 3d balls in a little bit as well.

Finally, the two statements for calculating *dx* and *x* could easily be combined into one. I’m only keeping them separate to be consistent with the math described above.

**A Program with both Analytical and Numerical Solutions**

For constant velocity problems the numerical approach gives the same results as the analytical solution, but that’s most definitely not going to be the case in the future, so to compare the two results more easily we can combine the two programs into one:

*motion-01.py*

from visual import *
# Initialize
x = 0.0
dt = 1.0
# Time loop
for t in arange(dt, 21, dt):
v = 0.5
# Analytical solution
x_a = v * t
# Numerical solution
dx = v * dt
x = x + dx
# Output
print t, x_a, x

which outputs:

>>>
1.0 0.5 0.5
2.0 1.0 1.0
3.0 1.5 1.5
4.0 2.0 2.0
5.0 2.5 2.5
6.0 3.0 3.0
7.0 3.5 3.5
8.0 4.0 4.0
9.0 4.5 4.5
10.0 5.0 5.0
11.0 5.5 5.5
12.0 6.0 6.0
13.0 6.5 6.5
14.0 7.0 7.0
15.0 7.5 7.5
16.0 8.0 8.0
17.0 8.5 8.5
18.0 9.0 9.0
19.0 9.5 9.5
20.0 10.0 10.0

Solving a problem involving acceleration comes next.

**Citing this post**: **Urbano**, L., 2016. Numerical versus Analytical Solutions, Retrieved February 26th, 2018, from *Montessori Muddle*: http://MontessoriMuddle.org/ .

**Attribution (Curator's Code )**: Via: ᔥ Montessori Muddle; Hat tip: ↬ Montessori Muddle.

Posted in Calculus, Mathematics, Physics, ProgrammingNo Comments » - Tags: calculus, differential equations, motion, numerical methods, programming

#### November 1, 2016

Skate park bowl under construction.

After batting around a number of ideas, three of my middle schoolers settled on building a model skate park out of popsicle sticks and cardboard for their interim project.

A lot of hot glue was involved.

The ramps turned out to be pretty easy, but on the second day they decided that the wanted a bowl, which proved to be much more challenging. They cut out sixteen profiles out of thicker cardboard, made a skeleton out of popsicle sticks, and then coated the top with thin, cereal-box cardboard.

When they were done they painted the whole thing grey–to simulate concrete I think–except for the sides, which were a nice flat blue so that they could put their own miniature graffiti over the top.

It was a lot of careful, well thought-out work.

Getting there: ramps, a rail, and bowl.

**Citing this post**: **Urbano**, L., 2016. Model Skate Park, Retrieved February 26th, 2018, from *Montessori Muddle*: http://MontessoriMuddle.org/ .

**Attribution (Curator's Code )**: Via: ᔥ Montessori Muddle; Hat tip: ↬ Montessori Muddle.

Posted in immersion, MathematicsNo Comments » - Tags: interim projects