In this project we analyzed my overall gait in terms of distance and time. The goal was to be able to create a predictive model that could be used to guess the heights of other people. We began by using an accelerometer app on our phones and attaching it to our chest. We then walked a certain distance and timed it. Later we formatted the data into a whole-class data sheet and created our predictive model from there.
Whole-Class Spreadsheet
https://docs.google.com/a/students.nusd.org/spreadsheets/d/150b1-yV3A_sxjVM_x5nBJOxQmjUJ6iwnHH4JuQfsgto/edit?usp=sharing
The Predictive Model
Our predictive model for determining height was using someone’s step length divided by the average ratio for of step length to height. So we calculated our step length by taking the distance we walked and divided it by the number of steps we took. We then divided the ratio .41 and got a height within half an inch or our own.
25.166666 / 10 = 2.516666 2.516666 / .41 = 6.138 ft.=6’1.7” Actual height: 6’2”
Reflection
A few challenges on the class data was mostly produced by the inconsistencies between groups. Many people attached their phones to different places. This would have produced different y-values, and subsequently, more inconsistencies. My team contributed equally to different parts of the project. We divided up the tasks such as formatting, graphing, and organizing the numbers. Our team unfortunately did little outside collaboration. We focused mostly on our numbers. However we did have a partner group sitting next to us. We both checked each other's charts to correctly organize all the numbers and eventually our predictive models. Our data restraints mostly included the little data we obtained. Many groups had several columns of numbers while we only had one complete set compared to other groups having three sets. We did encounter multiple hurdles right from the start. This included formatting the data from our phones to a spreadsheet all the way to simply finding our model's magic number. We only had one problem with google sheets. Like I previously mentioned the original conversion from our phones to the computer was the toughest part of using google sheets.
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Whole-Class Spreadsheet
https://docs.google.com/a/students.nusd.org/spreadsheets/d/150b1-yV3A_sxjVM_x5nBJOxQmjUJ6iwnHH4JuQfsgto/edit?usp=sharing
The Predictive Model
Our predictive model for determining height was using someone’s step length divided by the average ratio for of step length to height. So we calculated our step length by taking the distance we walked and divided it by the number of steps we took. We then divided the ratio .41 and got a height within half an inch or our own.
25.166666 / 10 = 2.516666 2.516666 / .41 = 6.138 ft.=6’1.7” Actual height: 6’2”
Reflection
A few challenges on the class data was mostly produced by the inconsistencies between groups. Many people attached their phones to different places. This would have produced different y-values, and subsequently, more inconsistencies. My team contributed equally to different parts of the project. We divided up the tasks such as formatting, graphing, and organizing the numbers. Our team unfortunately did little outside collaboration. We focused mostly on our numbers. However we did have a partner group sitting next to us. We both checked each other's charts to correctly organize all the numbers and eventually our predictive models. Our data restraints mostly included the little data we obtained. Many groups had several columns of numbers while we only had one complete set compared to other groups having three sets. We did encounter multiple hurdles right from the start. This included formatting the data from our phones to a spreadsheet all the way to simply finding our model's magic number. We only had one problem with google sheets. Like I previously mentioned the original conversion from our phones to the computer was the toughest part of using google sheets.
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