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Tinkerplots

Tinkerplots. Dr. Carryn Bellomo UNLV Department of Mathematical Sciences carryn.bellomo@ccmail.nevada.edu. Outline of Seminar. Overview of Tinkerplots Handouts Classroom Activities Conclusion. Overview of Tinkerplots. Helps you see trends and patterns in data

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Tinkerplots

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

  2. Outline of Seminar • Overview of Tinkerplots • Handouts • Classroom Activities • Conclusion

  3. Overview of Tinkerplots • Helps you see trends and patterns in data • Helps you make graphs and reports to present findings • There are sample data sets in: • health • science and nature • social studies • sports

  4. Overview of Tinkerplots • The program has several movies, located in the Help folder, 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 Tinker Plots Basics Movie!

  5. Handouts • The Tinkerplot Quick reference card will help you troubleshoot and will serve as a quick reference guide • The activity handouts (beginning with the Men’s 100m Dash) can be used as lesson plans in the classroom. This is taken from Exploring Data.pdf in the Help Folder. • Excel “How To” handout

  6. Classroom Activities • Old Faithful • Dinosaurs • Men’s 100-Meter Dash at the Olympics • Men and Women at the Olympics • Body Measurements • Australian Students • The Yo-Yo Mystery

  7. Activity – Old Faithful • Here we will explore 222 eruptions of Old Faithful, a geyser in Yellowstone National Park, WY, in August 1978 and 1979 • The data cards given have information on • Order number of observation • Year of observation • Day of month in August • Duration of eruption in minutes • Minutes to next eruption in minutes

  8. Activity – Old Faithful Let’s Think About This Before We Begin: • A sign posted at the Old Faithful visitor's center lets visitors know about how many minutes it will be to the next eruption. • To make these predictions, the park uses a formula that “proves to be accurate, plus or minus 10 minutes, 90% of the time.”

  9. Activity – Old Faithful • Open “Old Faithful.tp”Located in:Data and Demos Science and Nature • How should we organize the data to predict the time to next eruption?

  10. Activity – Old Faithful Investigate the Data: • There are two cases that do not have data on “how long to the next eruption time”. Case 106 and 222. • Let’s see if we can use our analysis to determine the time to the next eruption

  11. Activity – Dinosaurs • Here we will explore characteristics of dinosaurs • The data cards given have information on • Name of dinosaur • Food preference • Sub-classification • Type of hip, and teeth • Average adult length, height, and weight • Number of limbs used for walking • Continent lived on • Geological period lived in, and no. of years ago

  12. Activity – Dinosaurs Let’s Think About This Before We Begin: • Given the data we have, what kinds of questions can we answer about dinosaurs? • Do we expect any relationships between attributes? • Weight vs. height • Hip type vs. diet…

  13. Activity – Dinosaurs • Open “Dinosaurs.tp”Located in:Data and Demos Science and Nature • How should we organize the data to answer our questions?

  14. Activity – Dinosaurs Investigate the Data: • Over the years of their existence, did dinosaurs tend to get bigger, smaller, or stay the same size? • Did dinosaurs who ate meat (carnivores) tend to be larger or smaller than those who ate plants (herbivores)? If so, about how different in size were these two types?

  15. Activity – Men’s 100m Dash • Here we will explore the winning times for the men’s 100 meter dash from all the Olympic games from 1896 to 2000 • The data cards given have much more information than we actually need… all we are looking for here is the year and the men’s gold winning time for the 100m dash Pages 32-34 of Activity Book

  16. Activity – Men’s 100m Dash Let’s Think About This Before We Begin: • How many seconds do you think it would take the fastest man today to run 100m (about a football field)? • Do you think this time is different today than previous years? Why or why not?

  17. Activity – Men’s 100m Dash • Open “Olympics 100 Meter.tp”Located in:Data and Demos Exploring Data Starters • How should we configure our plot to answer our questions?

  18. Activity – Men’s 100m Dash Investigate the Data: • When was there the greatest change in running times? Why do you think this is so? • What has happened to the running times over the years? How could you account for this? • Can you project the times for future years?

  19. Activity – Men’s 100m Dash Side Note – Using Excel to Add Trendlines: • You can add Linear and exponential function trendlines in MS Excel • This adds formality to the questions we asked previously

  20. Using Excel – Men’s 100m Dash • Copy the data and paste it into Excel • Add a chart (scatterplot) • Use excel to find the linear approximation (least squares line) • Use the equation to graph your own line, and use it to predict missing or future values “How To” specifics are on your excel handout

  21. Activity – Men vs. Women • Here we will be comparing the winning times/distances of men and women in the Olympics over the years • The data cards given have the gold medal times for men and women for each year in • 100m • 200m • High jump and • Long jump Pages 35-36 of Activity Book

  22. Activity – Men vs. Women Let’s Think About This Before We Begin: • Do you think there will be a difference in the 100m dash between men and women? If so, why, and by how much? • If there is a difference between gold medal times/distances, is it consistent throughout the years? Or perhaps is the gap getting smaller (or larger)?

  23. Activity – Men vs. Women • Open “Olympics Men Women.tp”Located in:Data and Demos Exploring Data Starters • How should we configure the data to get the best answers to our questions?

  24. Activity – Men vs. Women Investigate the Data: • With the 100m dash, does one gender out perform another? If yes, by how much, and is this consistent through the years? • Let’s look at the 200m dash… • Let’s look at an Excel file with this information

  25. Activity – Body Measurements • Here we will explore characteristics of Body measurements of adults (247 men and 260 women) who were exercising several hours a week • The data cards given have information on: • Gender, age, weight, height • Pelvic diameter in centimeters • Chest depth and diameter in centimeters • Elbow, wrist, knee and ankle diameter in centimeters • Shoulder, chest, waist, abdominal, hip, thigh, bicep, forearm, knee, calf, ankle and wrist girth in centimeters

  26. Activity – Body Measurements Let’s Think About This Before We Begin: • Given the data we have, what kinds of questions can we answer about these measurements? • Do we expect any relationships between attributes?

  27. Activity – Body Measurements • Open “Body Measurements.tp”Located in:Data and Demos Health • How should we organize the data to answer our questions?

  28. Activity – Body Measurements Investigate the Data: • For which measurement are males and females most different? • For which measurement are they most alike? Explain.

  29. Activity – Australian Students • Here we will explore characteristics of 159 students in south Australia in grades 8-12 • The data cards given have information on: • Gender, height in cm, birthday, grade level • Type of school, public/private • Travel time to school • Number of people in household • Type of driver's license, whether they have their own car • Whether they have their own mobile phone • Whether they have access to home computer, and Internet • Weekday hours spent watching TV before school

  30. Activity – Australian Students Let’s Think About This Before We Begin: • Given the data we have, what kinds of questions can we answer about these students? • Do we expect any relationships between attributes?

  31. Activity – Australian Students • Open “Australian Students.tp”Located in:Data and Demos Social Studies • How should we organize the data to answer our questions?

  32. Activity – Australian Students Investigate the Data: • Is there evidence in these data of a gender difference in pay? If so, about how big is the difference? • What differences do you notice, if any, between public school students and private school students?

  33. Activity – The YoYo Mystery • Here we will be looking at computer driven data from a yo-yo factory to determine when a break in occurred • The data cards given have • Elapsed time • Hour • Period • Number_YoYos • Group Pages 37-39 of Activity Book

  34. Activity – The YoYo Mystery Let’s Think About This Before We Begin: • The manager is the prime suspect; he is the last to leave and first to arrive. When are his whereabouts unaccounted for?

  35. Activity – The YoYo Mystery • Open “Yo Yo Mystery.tp”Located in:Data and Demos Exploring Data Starters • How should we configure the data to determine when the break-in occurred?

  36. Activity – The YoYo Mystery Investigate the Data: • About what time could the break in have taken place? • With this information, could the suspect have committed the break-in?

  37. Conclusion

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

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