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Which Quarterback is the G.O.A.T.? - Using Statistical Calculations

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Grade Levels
6th - 9th
Standards
Formats Included
  • Google Slidesâ„¢
Pages
25 pages
$15.00
$15.00
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Description

Note: This is an upgraded version of the Statistical Analysis Using Football (QB) Stats. If you have previously purchased this resource please email me and I will send you the updated version.

This project has students calculate percents, averages, and ratios, as well as create scatterplots, box and whisker plots, and histogram (bar graphs) based on their calculations.

Google slideshow includes 12 well know Hall of Fame quarterbacks including Joe Namath, Roger Staubach, Terry Bradshaw, Joe Montana, John Elway, Dan Marino, Steve Young, Jim Kelly, Troy Aikman, Brett Farve, Peyton Manning, Drew Brees, and the latest stats to include Tom Brady's last year in the NFL (2022).

Links to the Google sheets, student copy and teacher answer key, can be found on the last slide of the slideshow.

There is also a link to a 10 question Quizizz based on student calculations found on the first page of the Teacher's Answer Key.

Total Pages
25 pages
Answer Key
Included
Teaching Duration
1 Week
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Standards

to see state-specific standards (only available in the US).
Represent data with plots on the real number line (dot plots, histograms, and box plots).
Use statistics appropriate to the shape of the data distribution to compare center (median, mean) and spread (interquartile range, standard deviation) of two or more different data sets.
Interpret differences in shape, center, and spread in the context of the data sets, accounting for possible effects of extreme data points (outliers).
Use the mean and standard deviation of a data set to fit it to a normal distribution and to estimate population percentages. Recognize that there are data sets for which such a procedure is not appropriate. Use calculators, spreadsheets, and tables to estimate areas under the normal curve.
Summarize categorical data for two categories in two-way frequency tables. Interpret relative frequencies in the context of the data (including joint, marginal, and conditional relative frequencies). Recognize possible associations and trends in the data.

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