Gait-Height Model
Data:
Reflection:
The over-arching challenge that I encountered while working on the Whole-Class Gait database was the amount of data that each student had. Most students had around 100 lines of data for each trial, and every student had to run at least 3 trials. I think that everyone within my team had an equal say during both; the experiment and the data analysis. My group asked other teams clarification questions to make sure that we were interpreting their data correctly, and to make sure that we weren't making any false assumptions. It's extremely important to have the correct data, and to make sure everything is labeled correctly. I needed to know the key pieces of data to make the best representation. I learned that the x-acceleration was most accurate, and therefore making it the most important piece of data for this individual experiment. I decided to only use the x data and compare it to the height of the student to make the data more clear. The lack of organization among that data was definitely a shortcoming that I had to work with. I didn't encounter many challenges while performing the gait experiment. My group filmed each other while performing the experiment, which allowed us to make sure that all of our data was accurate, and we were able to limit the amount of human error. If we were to do this experiment again I'd definitely film each person again because it enabled us to minimize human errors and maximize data consistency. I encountered huge software constraints while working with Google Sheets. It was very difficult to make a model of the whole class's data. The graphs it designed weren't organized and were extremely hard to read. I'm still in the process of bettering my gait-height model, and I'm trying to make it more legible.
The over-arching challenge that I encountered while working on the Whole-Class Gait database was the amount of data that each student had. Most students had around 100 lines of data for each trial, and every student had to run at least 3 trials. I think that everyone within my team had an equal say during both; the experiment and the data analysis. My group asked other teams clarification questions to make sure that we were interpreting their data correctly, and to make sure that we weren't making any false assumptions. It's extremely important to have the correct data, and to make sure everything is labeled correctly. I needed to know the key pieces of data to make the best representation. I learned that the x-acceleration was most accurate, and therefore making it the most important piece of data for this individual experiment. I decided to only use the x data and compare it to the height of the student to make the data more clear. The lack of organization among that data was definitely a shortcoming that I had to work with. I didn't encounter many challenges while performing the gait experiment. My group filmed each other while performing the experiment, which allowed us to make sure that all of our data was accurate, and we were able to limit the amount of human error. If we were to do this experiment again I'd definitely film each person again because it enabled us to minimize human errors and maximize data consistency. I encountered huge software constraints while working with Google Sheets. It was very difficult to make a model of the whole class's data. The graphs it designed weren't organized and were extremely hard to read. I'm still in the process of bettering my gait-height model, and I'm trying to make it more legible.