Engaging students: Approximating data by a straight line

In my capstone class for future secondary math teachers, I ask my students to come up with ideas for engaging their students with different topics in the secondary mathematics curriculum. In other words, the point of the assignment was not to devise a full-blown lesson plan on this topic. Instead, I asked my students to think about three different ways of getting their students interested in the topic in the first place.

I plan to share some of the best of these ideas on this blog (after asking my students’ permission, of course).

This student submission again comes from my former student Esmerelda Sheran. Her topic, from Algebra: approximating data by a straight line.

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A.2) How could you as a teacher create an activity or project that involves your topic?

 

If I created an activity for my class over approximating data by using a straight line I would make sure the type of data, they use is something that is relevant or interesting in the student’s lives. I would have the students work in pairs and choose the data they would work with out of three sets of data I have chosen. Examples of the choices of data would be the relationships between interceptions and wins for NFL teams, car accidents and age, or attendance and GPA (in college/universities). Using the data they chose the students would first take an educated guess of how the graph would look like, draw the scatter plot associated with the data, and compare their guess to the actual graph. At that point the students would try to identify the parent function (xb+c, mx+b, ab, ln(x) etc.) that the data is most similar to or if the data even has correlation. They would then draw what they believed the best fit line would look like on the scatterplot which they would compare to the linear regression once they calculated it on a graphing calculator. I would hope that this activity would be interesting due to the data being real and relatable as well as it being a way to connect parent functions and statistical data.

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D.1) What interesting things can you say about the people who contributed to the discovery and/or the development of this topic?

Two of the main collaborators of linear regression are Sir Francis Galton and Karl Pearson. Galton was the discoverer of the linear regression and Pearson further elaborated on Galton’s ideas. Linear regression actually came to be because of sweet peas, Galton was studying heredity in sweet peas and formulated linear regression to aid him in studying the relations he found in his studies. Galton was much more than a hereditist, he was a geologist, meteorologist, tropical explorer, founder of differential psychology, inventor of fingerprint identifications, and an author. A few more interesting things about Galton is that he was knighted, he was accused of promoting eugenics, he was British and he was a half cousin of Charles Darwin. If you were wondering what “eugenics” is, it is the idea of planned breeding of humans through selectively breeding and sterilization. Galton once said, “… I object to pretensions of natural equality.” Being that Galton studied heredity it is no wonder that he felt that some physical/mental/emotional attributes where superior and that humans would benefit from having the “best” genes. Unfortunately for Galton eugenics was frowned upon and he was attacked for promoting it. I think that students would find Galton extremely interesting because of his wide variety of interests.

Karl Pearson, although not as complex as Galton had a few attributes that I feel would interest students. Pearson did not have a childhood that would be considered normal in modern day. Pearson was homeschooled up until he turned nine, and then he went to London alone to study at the University of College School. After he received his degrees and studied physics, metaphysics and Darwinism, Pearson developed his own view in social Darwinism. The social beliefs, he developed led him to changing his name from Carl to Karl.

 

 

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E.1) How can technology be used to effectively engage students with this topic?

 

Technology in the classroom has and always will be an effective way to engage students if used correctly. To engage my students to learn how to approximated data with a straight line I would use excel, a smartboard, or the khan academy website. Excel is a useful piece of technology that is underappreciated by the average Joe. With a set of data you can record the relationships and then use the tools to create a scatterplot and then find the linear regression line on the graph.

Using a smartboard in the classroom is effective because it is new technology that is very special and kind of rare. Using smartboard to graph the points of data and then drawing an approximated regression line is highly kinesthetic and gives hands-on experiences instead of just typing in number and getting a calculated result that required almost no brain power. Kinesthetically moving their arms up, down, or side to side helps the students get a feel for the variation and relations between the data and drawing a best fit line themselves help the student understand the data on a different level. The Khan Academy website is a great resource for being introduced and even mastering the concept of linear regression because of the different activities available. For visual and auditory learners, there are a series of videos that explain approximating data by linear regression as well as how to be the most accurate when approximating. Similarly, there is an activity for kinesthetic learners in which they can move a line around to see which line seems most like the best fit line. It is beneficial from an instructor to use this website to help students of all learning types.

 

References

http://www.mirror.co.uk/news/uk-news/elderly-priest-found-dead-after-5099110

https://www.dartmouth.edu/~matc/math5.geometry/unit2/unit2.html

http://geomhistory.com/home.html

http://www.americanegypt.com/feature/cities/chichenitza/castillo_shadow.htm

https://explorable.com/greek-geometry

 

 

What Happens if the Explanatory and Response Variables Are Sorted Independently?

From the category “I Can’t Believe What I Just Read,” the following question was posed to a question-and-answer statistics board last month:

Suppose we have data set (X_i,Y_i) with n points. We want to perform a linear regression, but first we sort the X_i values and the Y_i values independently of each other, forming data set (X_i,Y_j). Is there any meaningful interpretation of the regression on the new data set? Does this have a name?

I imagine this is a silly question so I apologize, I’m not formally trained in stats. In my mind this completely destroys our data and the regression is meaningless. But my manager says he gets “better regressions most of the time” when he does this (here “better” means more predictive). I have a feeling he is deceiving himself.

The answers were priceless:

Your intuition is correct: the independently sorted data have no reliable meaning because the inputs and outputs are being randomly mapped to one another rather than what the observed relationship was.

There is a (good) chance that the regression on the sorted data will look nice, but it is meaningless in context.

And:

If you want to convince your boss, you can show what is happening with simulated, random, independent x,y data. With R:

And:

This technique is actually amazing. I’m finding all sorts of relationships that I never suspected. For instance, I would have not have suspected that the numbers that show up in Powerball lottery, which it is CLAIMED are random, actually are highly correlated with the opening price of Apple stock on the same day! Folks, I think we’re about to cash in big time. 🙂

The sad end of the story, from the original poster:

Thank you for all of your nice and patient examples. I showed him the examples by @RUser4512 and @gung and he remains staunch. He’s becoming irritated and I’m becoming exhausted. I feel crestfallen. I want my work to mean something. I will probably begin looking for other jobs soon.

Engaging students: Approximating data by a straight line

In my capstone class for future secondary math teachers, I ask my students to come up with ideas for engaging their students with different topics in the secondary mathematics curriculum. In other words, the point of the assignment was not to devise a full-blown lesson plan on this topic. Instead, I asked my students to think about three different ways of getting their students interested in the topic in the first place.

I plan to share some of the best of these ideas on this blog (after asking my students’ permission, of course).

This student submission again comes from my former student Delaina Bazaldua. Her topic, from Algebra: approximating data to a straight line.

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How has this topic appeared in pop culture (movies, TV, current music, video games, etc.)?

One of my favorite shows to watch is How I Met Your Mother. I specifically chose this topic for this class because of how it relates to an episode of the show. A piece of the episode that I’m referring to is shown in the YouTube video:

Barney, one of the main characters, describes the graph as the Crazy/Hot Scale. According to him, a girl cannot be crazier than hot which means she has to be above the diagonal straight line. This relates to the topic because one can approximate data by the straight line that Barney gives the viewer. I think the students will be able to relate to this and also find it humorous. Because this video has both of these characteristics, they will be able to remember the concept for upcoming homework and tests which is ultimately the most important part of math: understanding it and being able to recall it.

 

 

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How has this topic appeared in the news?

Most lines are drawn for the purpose of seeing if there is a relationship between the x and y axis and trying to figure out if you can approximate data from the straight line that is drawn. Graphs like this are found all over the news, and they often relate to natural disasters. For example, this linear regression, http://d32ogoqmya1dw8.cloudfront.net/images/quantskills/methods/quantlit/bestfit_line.v2.jpg, describes floods. In http://serc.carleton.edu/mathyouneed/graphing/bestfit.html, where the picture is found, describes more activities that can be used to create a linear regression which can be converted into a straight line. These examples of straight lines can be used to find more data that isn’t necessarily shown from the points that are plotted. The examples the website gave are: flood frequency curves, earthquake forecasting, meteorite impact prediction, earthquake frequency vs. magnitude, and climate change. This is beneficial for math because it allows students to realize that math isn’t abstract like it is often perceived to be, but rather, it is used for something very important and something that occurs several times a year such as natural disasters and weather.

 

 

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How can this topic be used in your students’ future courses in mathematics or science?

One of the purposes for teachers to teach is for students to learn what they should for the following year so they can be successful in the particular topic. When it comes to approximating data based on a straight line, the knowledge a student learns in algebra will carry them through statistics, physics, and other higher math and science classes. Linear regression is shown in statistics as one can see in this statistics website: http://onlinestatbook.com/2/regression/intro.html while physics is represented in the physics website: http://dev.physicslab.org/Document.aspx?doctype=3&filename=IntroductoryMathematics_DataAnalysisMethods.xml. A lot can be predicted from these straight lines which is why these graphs aren’t foreign to upper level math and science classes. As I stated before, a lot can be predicted from the graph where data points aren’t necessarily on the trend the data is setting which allows students to expect what would occur at a particular x or y value. A background in this area can help students through the rest of school and perhaps even the rest of their life in some cases.

 

References:

https://www.youtube.com/watch?v=uN_sSXKbzHk

http://serc.carleton.edu/mathyouneed/graphing/bestfit.html

http://onlinestatbook.com/2/regression/intro.html

http://dev.physicslab.org/Document.aspx?doctype=3&filename=IntroductoryMathematics_DataAnalysisMethods.xml