Will You Live to be 110?

This diagram created using Inspiration® 8.0 by Inspiration Software®, Inc.

 

 

Will I live to be 110?

I.     Class

A.    Astronomical Issues

1.    When will the sun die?  Researching what the function is, what all the variables mean, and solving the function (Regression and function patterns). 

B.    Environmental Issues – Exponential/Log Functions (extinction rates, etc.)

1.    Global Warming – Discussing when and how models are useful, and what limitations they have.

2.    Natural Disasters – Looking at what affects your chances of being in a natural disaster with statistics (i.e. if you live in Kansas, youÕre much more likely to be in a tornado than if you live in Maine, or if you live on a river, youÕre more likely to be in a flood).

C.    Population Density – Exponential Model, population growth curves, predicting future values.

D.    Genetics – Using correlation and regression to discover how much your family history affect your likelihood of getting certain diseases and conditions.  If time, maybe the students could even conduct an experiment of their own.

II.   Groups – For the group work, we hope to come up with some kind of general statistical guidelines.  The effects of most of the topics below (if not all) would be investigated using regression, function patterns, correlation, and basic statistical measure (mean, median, mode, standard deviation/variance).

A.    Diseases – Using Statistics to calculate probabilities, looking at correlations to determine who gets what disease.

B.    Lifestyle Choices

1.    Health

a.    Exercise – Calories burned as a function of exercise, lifespan as a function of amount of exercise.

b.    Substance use – Statistics on alcohol, drug, and other substance abuse as related to lifespan.

c.     Diet – functions with multiple variables (a=candy bars, b=fruit, c=serving of peanuts, d=sandwich, etc.) where we know average caloric content of each.  Students can make a function of their daily calorie intake based on what types (and how many of each,) different foods they eat.

d.    Doctor visits - Looking at how number (frequency) of doctor visits relates to lifespan.

2.    Risk Behavior – reckless/drunk driving, accidents during thrill seeking activities, etc.  use statistics to calculate probabilities, then use functions to calculate an overall picture.  (for example, if 1/100,000 people die skydiving, and a person plans to skydive 5 times in their life, their chance of dying skydiving would be 5(1/100,000), and that would be one piece of their larger function of if they will die prematurely.  Add a bunch of risky behaviors up like that. 

3.    Sleep – If/Then Logic (If I get enough sleep, ThenÉ) Also, more regression and correlation studies on how sleep affects lifespan, and quality of life.

4.    Personal

a.    Stress- Statistics again, also maybe a little bit of research on how to reduce stress levels.  Students should look at studies on reducing stress level and explain which methods for stress reduction work best based on the statistical results of the study.

b.    Proximity to loved ones – coordinate geometry – bringing algebra and geometry concepts together  (distance formula, how many relatives live in a five mile radius, etc.) 

C.    Demographics-All of these subtopics are more of the same, using correlation, regression, and basic statistical measures to research how these things affect lifespan.

1.    Marital Status -  See if married people or single people live longer using the same procedures outlined above.

2.    Ethnicity – There is an abundance of data on the internet that compares life expectancy of different race groups in different countries.

3.    Occupation/Finances – In addition to the kinds of studies outlined above, we could get into the interest rate formulas, and talk about how a small investment while in high school would become tens of thousands of dollars by retirement, and how to know what deal is best when buying a car.

4.    Location – Perhaps we could use coordinate geometry for this as well. For example, maybe proximity to a golf course would be correlated with longer life span (not causally related) because typically golf courses are located in wealthy neighborhoods and wealthy people probably live slightly longer.