Also, find r2 (coefficient of determination) and r (correlation coefficient). Discuss your findings. Your topic may be that is related to sports,...
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(LR-2) Plot the points (x, y) to obtain a scatterplot. Use an appropriate

scale on the horizontal and vertical axes and be sure to label carefully. 
Visually judge whether the data points exhibit a relatively linear trend. (If so, proceed. If not, try a different topic or data set.)

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Instructions For this assignment, collect data exhibiting a relatively linear trend, fnd the line o± best ft, plot the data and the line, interpret the slope, and use the linear equation to make a prediction. Also, fnd r 2 (coe²cient o± determination) and r (correlation coe²cient). Discuss your fndings. Your topic may be that is related to sports, your work, a hobby, or something you fnd interesting. I± you choose, you may use the suggestions described below. A Linear Model Example and Technology Tips are provided in separate documents. Tasks for Linear Regression Model (LR) (LR-1) Describe your topic, provide your data, and cite your source. Collect at least 8 data points. Label appropriately. (Highly recommended: Post this information in the Linear Model Project discussion as well as in your completed project. Include a brief informative description in the title of your posting. Each student must use di³erent data.) The idea with the discussion posting is two-±old: (1) To share your interesting project idea with your classmates, and (2) To give me a chance to give you a brie± thumbs-up or thumbs-down about your proposed topic and data. Sometimes students get o³ on the wrong ±oot or misunderstand the intent o± the project, and your posting provides an opportunity ±or some ±eedback. Remark: Students may choose similar topics, but must have diFerent data sets . For example, several students may be interested in a particular Olympic sport, and that is fne, but they must collect di³erent data, perhaps ±rom di³erent events or di³erent gen der. (LR-2) Plot the points (x, y) to obtain a scatterplot. Use an appropriate scale on the horizontal and vertical axes and be sure to label care±ully. Visually judge whether the data points exhibit a relatively linear trend. (I± so, proceed. I± not, try a di³erent topic or data set.) (LR-3) Find the line o± best ft (regression line) and graph it on the scatterplot. State the equation o± the line. (LR-4) State the slope o± the line o± best ft. Care±ully interpret the meaning o± the slope in a sentence or two. (LR-5) Find and state the value o± r 2 , the coe²cient o± determination, and r, the correlation coe²cient. Discuss your fndings in a ±ew sentences. Is r positive or negative? Why? Is a line a good curve to ft to this data? Why or why not? Is the linear relationship very strong, moderately strong, weak, or nonexistent? (LR-6) Choose a value o± interest and use the line o± best ft to
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make an estimate or prediction. Show calculation work. (LR-7) Write a brief narrative of a paragraph or two. Summarize your Fndings and be sure to mention any aspect of the linear model project (topic, data, scatterplot, line, r, or estimate, etc.) that you found particularly important or interesting. You may submit all of your project in one document or a combination of documents, which may consist of word processing documents or spreadsheets or scanned handwritten work, provided it is clearly labeled where each task can be found. Be sure to include your name. Projects are graded on the basis of completeness, correctness, ease in locating all of the checklist items, and strength of the narrative portions. Here are some possible topics: Choose an Olympic sport -- an event that interests you. Go to http://www.databaseolympics.com/ and collect data for winners in the event for at least 8 Olympic games (dating back to at least 1980). (Example: Winning times in Men's 400 m dash). Make a quick plot for yourself to "eyeball" whether the data points exhibit a relatively linear trend. (If so, proceed. If not, try a di±erent event.) After you Fnd the line of best Ft, use your line to make a prediction for the next Olympics (2014 for a winter event, 2016 for a summer event ). Choose a particular type of food . (Examples: ²ish sandwich at fast-food chains, cheese pizza, breakfast cereal) ²or at least 8 brands, look up the fat content and the associated calorie total per serving. Make a quick plot for yourself to "eyeball" whether the data exhibit a relatively linear trend. (If so, proceed. If not, try a di±erent type of food.) After you Fnd the line of best Ft, use your line to make a prediction corresponding to a fat amount not occurring in your data set.) Alternative: Look up carbohydrate content and associated calorie total per serving. Choose a sport that particularly interests you and Fnd two variables that may exhibit a linear relationship. ²or instance, for each team for a particular season in baseball, Fnd the total runs scored and the number of wins. Excellent websites: http://www.databasesports.com/ and http://www.baseball-reference.com/
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Subject: Math
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