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Tinkerplots II

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  1. Tinkerplots II Dr. Carryn Bellomo UNLV Department of Mathematical Sciences carryn.bellomo@ccmail.nevada.edu

  2. Outline of Seminar • More on Tinkerplots • Classroom Activities • Conclusion

  3. More on Tinkerplots • We have seen how Tinkerplots helps you see trends and patterns in data • Today we will look at several activities, and also build our own data set

  4. More on Tinkerplots • The program has several movies, such as: • Adding Data (entering your own data set) • Comparing Groups (Occupational Data) • Exploring Relationships (Ozone Levels) • Making Common Graphs (Student Data) • Tinkerplot Basics (Cat Data) Let’s view the Adding Data Movie! All movies located in: Tinkerplots Help  Movies

  5. Handouts • The Tinkerplot Quick reference card will help you troubleshoot and will serve as a quick reference guide • Excel “How To” handout

  6. Classroom Activities One More Example: • Heaviest Backpacks Getting Data from the Web: • Teacher Behaviors • Education Levels • Smoking and Cancer • Highway Deaths (in Excel) Using Data Collected from the Class: • Popularity

  7. Activity – Heaviest Backpacks • Here we will explore the backpack weights of students • The data cards given have information on • First name of student • Gender of student • Grade level of student • Weight of student in pounds • Weight of student’s backpack in pounds

  8. Activity – Heaviest Backpacks • Open “Heaviest Backpacks.tp”Located in:Data and Demos Exploring Data Starters • What kind of relationships do we expect to find? • How should we organize the data?

  9. Activity – Heaviest Backpacks Investigate the Data: • Is there a relationship between packweight and grade? Compare the mean packweight for each grade level. • Do girls tend to carry lighter backpacks than boys? • Does a person who weighs more carry a heavier pack?

  10. Activity – Teacher Behaviors • What instructor behaviors do students feel are more likely to contribute to their academic success? • Data was taken from 215 business students at 9 colleges asking them to rank 50 instructor behaviors. • They gave each of the behaviors a rank: Important, Not Important, Neither

  11. Activity – Teacher Behaviors The data can be found at : http://lib.stat.cmu.edu/DASL/Datafiles/InstructorBehavior.html Before We Begin : • Look at the data collection process • What kinds of questions can we answer with this data set?

  12. Activity – Teacher Behaviors Get the data into Tinkerplots: • Highlight the data on the webpage (including column titles) • Copy the data by holding down the Control key and pressing C • Go to a blank page in Tinkerplots • Pull out a stack of data cards • Go to Edit, then Paste Cases

  13. Activity – Teacher Behaviors Investigate the Data to Answer: • What are the 5 most important instructor behaviors? • What are the 5 least important instructor behaviors? • Of all the behaviors listed, what ‘ranking’ surprises you the most?

  14. Activity – Education Levels • What age groups are more likely to have higher levels of education? 25-34, 35-44, 45-54, 55-64 or 65+ • Data on educational attainment was taken in 1984 by the US Census • Data is categorized as no. of Americans in each age group (in thousands) who have: 1-3 years college, 4+ years college, completed HS, did not complete HS

  15. Activity – Education Levels The data can be found at : http://lib.stat.cmu.edu/DASL/Datafiles/Educationbyage.html Before We Begin : • Look at the data collection process • What kinds of questions can we answer with this data set?

  16. Activity – Education Levels Get the data into Tinkerplots: • Highlight the data on the webpage (including column titles) • Copy the data by holding down the Control key and pressing C • Go to a blank page in Tinkerplots • Pull out a stack of data cards • Go to Edit, then Paste Cases

  17. Activity – Education Levels Investigate the Data to Answer: • For 1984, what age group has the most people with 4+ years of college? • What age group has the most high school dropouts? • To what social events can you attribute to these patterns?

  18. Activity – Smoking and Cancer • Is there a link between smoking and certain types of cancers? • The data collected are per capita numbers of cigarettes smoked (sold) by 43 states and the District of Columbia in 1960 together with death rates (per thousand) of population from various forms of cancer.

  19. Activity – Smoking and Cancer The data can be found at : http://lib.stat.cmu.edu/DASL/Datafiles/cigcancerdat.html Before We Begin : • Look at the data collection process • What kinds of questions can we answer with this data set?

  20. Activity – Smoking and Cancer Get the data into Tinkerplots: • Highlight the data on the webpage (including column titles) • Copy the data by holding down the Control key and pressing C • Go to a blank page in Tinkerplots • Pull out a stack of data cards • Go to Edit, then Paste Cases

  21. Activity – Smoking and Cancer Investigate the Data to Answer: • Is there a correlation between smoking and each of these cancer types? • Which two states had the highest number of cigarettes smoked (sold)? • Which state has the highest incidence of each cancer type? • Nevada and DC are outliers, why? • What state(s) are statistically bad to live in?

  22. Activity – Highway Deaths • New Mexico highway fatality rate is highly related to the U.S. fatality rate • Data was taken from U.S. and New Mexico highway fatalities (per million vehicle-miles) from 1945-1984.

  23. Activity – Highway Deaths The data can be found at : http://lib.stat.cmu.edu/DASL/Datafiles/hwfataldat.html Before We Begin : • Look at the data collection process • What kinds of questions can we answer with this data set?

  24. Activity – Highway Deaths Here we will be using MS Excel: • Copy the data and paste it into Excel • Add a chart (scatterplot) “How To” specifics are on your excel handout

  25. Activity – Highway Deaths Investigate the Data to Answer: • Is there a relationship between these two data sets? What do you attribute this to? • Find the trendline for each set of data. Is the “gap” getting smaller or larger? How do you interpret this?

  26. Activity – Popularity • What factors are important for popularity according to 4-6 grade students? grades, sports, looks, or money • Data was collected which asked students: • Out of “grades, sports or looks” what is most important to you? • Rank above in order of their importance for popularity (1-4)

  27. Activity – Popularity Collect data and bring it into Tinkerplots: • Develop a questionnaire, and distribute it to the class • Add the data cards to Tinkerplots • Open a new document • Drag out a table, and enter attributes • Enter data fields for each data point

  28. Activity – Popularity Similar data can be found at: http://lib.stat.cmu.edu/DASL/Datafiles/PopularKids.html Before We Begin : • Look at the data collection process • What kinds of questions can we answer with this data set?

  29. Activity – Popularity Get the data into Tinkerplots: • Highlight the data on the webpage (including column titles) • Copy the data by holding down the Control key and pressing C • Go to a blank page in Tinkerplots • Pull out a stack of data cards • Go to Edit, then Paste Cases

  30. Activity – Popularity Investigate the Data to Answer: • What is most important to all students? • Is this the same for males and females? • On average, order the rankings on “importance for popularity” for all students. What is most important? By how much? What is least important? By how much • Is this different for males and females?

  31. Conclusion

  32. Conclusion • This presentation and handouts can be found at:http://www.unlv.edu/faculty/bellomo