Loading presentation...
Prezi is an interactive zooming presentation

Present Remotely

Send the link below via email or IM

Copy

Present to your audience

Start remote presentation

  • Invited audience members will follow you as you navigate and present
  • People invited to a presentation do not need a Prezi account
  • This link expires 10 minutes after you close the presentation
  • A maximum of 30 users can follow your presentation
  • Learn more about this feature in our knowledge base article

Do you really want to delete this prezi?

Neither you, nor the coeditors you shared it with will be able to recover it again.

DeleteCancel

Make your likes visible on Facebook?

Connect your Facebook account to Prezi and let your likes appear on your timeline.
You can change this under Settings & Account at any time.

No, thanks

Bivariate Data Project

No description
by

Emma Dennis

on 11 March 2013

Comments (0)

Please log in to add your comment.

Report abuse

Transcript of Bivariate Data Project

Bivariate Data Project By: Emma Dennis The graph of my data has a positive association. It’s kind of hard to tell because many people interpret it to be a straight line, but if you look really carefully, it’s a positive association. It’s awfully strong, with outliers that are close to the line of best fit. I graphed the top 50 runner's age and time on a scatter plot to see if there was any association. The data I used was from the 2012 Franklin Run Walk Crawl. The Research Question: Association? The Graph: The Data: Does your age affect your time in a 5k? http://www.coolrunning.com/results/12/ma/Jul21_WillNo_set1.shtml Correlation Coefficient: r=.1259175601 Residuals? In this example there are residuals for many reasons. One, being because we have a lurking variable. We do not know the exact physical condition of each runner. This is why there are older runners placing higher than younger runners. The older runners may have more experience, and that may be why they are placing higher than the more inexperienced runners. Conclusion: In the end, a person’s age does affect their time in the 5K. The graph seems to be a straight line because we do not know the physical condition of each runner (the lurking variable), but we can infer that the age of a runner does affect their time. All in all, there is a better chance that a younger runner ran faster than someone older than them. Correlation Determination: r²= .0158552319 LSRL: y=0.19x+20.47
Full transcript