The data shows that there is a slightly positive association between the length of a shoe and the height of its heel. Had I done all the same types of shoes, (sneakers, flats, boots, etc) maybe it would have been stronger however given that I used all types the association is about what I expected it to be. There are many outliers which I believe are shoes that belong to other Danielle’s Dad vs the rest of her family, and boots that had a higher heel height than the rest.

I am seeing a slight pattern within my data so I am going to try and straighten it.

Once I made all my graphs I noticed that there is association between shoe length and heel height so it’s not possible to completely straighten the data. I chose the graph with the lowest residual on the y axis.

I think it will be nonlinear, and not a very strong association.

Here’s a link to my data. __https://docs.google.com/a/oxbowhs.org/spreadsheets/d/1RZdSKghFd78h_HbREbvfYrhmBNPNrNrfE22HirWb9wo/edit?usp=sharing__

Regression equation: = 7.9298406 + 0.1053255X

Here’s a graph of the residuals vs. the x values.

The data shows that there is a slightly positive association between the length of a shoe and the height of its heel. Had I done all the same types of shoes, (sneakers, flats, boots, etc) maybe it would have been stronger however given that I used all types the association is about what I expected it to be. There are many outliers which I believe are shoes that belong to other Danielle’s Dad vs the rest of her family, and boots that had a higher heel height than the rest.

I am seeing a slight pattern within my data so I am going to try and straighten it.

Once I made all my graphs I noticed that there is association between shoe length and heel height so it’s not possible to completely straighten the data. I chose the graph with the lowest residual on the y axis.