A statistics lesson on describing and making claims from data representations, specifically linearly increasing data. Applies ideas of rate-of-change to develop writing a linear equation to fit the data, using the equation to interpolate and extrapolate additional information, and integrating the mathematical interpretation appropriately into a social sciences argument.
Students apply their knowledge of linear regression and design to solve a real-world challenge to create a better packing solution for shipping cell phones. They use different materials, such as cardboard, fabric, plastic, and rubber bands to create new “composite material” packaging containers. Teams each create four prototypes made of the same materials and constructed in the same way, with the only difference being their weights, so each one is fabricated with a different amount of material. They test the three heavier prototype packages by dropping them from different heights to see how well they protect a piece of glass inside (similar in size to iPhone 6). Then students use linear regression to predict from what height they can drop the fourth/final prototype of known mass without the “phone” breaking. Success is not breaking the glass but not underestimating the height by too much either, which means using math to accurately predict the optimum drop height.
Students will breed fruit flies through several generations and record their data using mathematical models in order to demonstrate the inheritance of trait variations.
Students groups act as NASA/GM engineers challenged to design, build and test robotic hands, which are tactile feedback systems made from cloth gloves and force sensor circuits. Student groups construct force sensor circuits using electric components and FlexiForce sensors to which resistance changes based on the applied force. They conduct experiments to find the mathematical relationship between the force applied to the sensor and the output voltages of the circuit. They take several measurements force vs. resistance, force vs. voltage and use the data to find the best fit curve models for the sensor. Different weights applied to the sensor are used as a scalable force. Students use traditional methods and current technology (calculators) to plot the collected data and define the curve equations. Students test their gloves and use a line of best fit to determine the minimum force required to crack an egg held between the index finger and thumb. A PowerPoint(TM) file and many student handouts are included.
Students are introduced to several types of common medical sensor devices, such as ear and forehead thermometers, glucometers and wrist blood pressure monitors; they use the latter to measure their blood pressure and pulse rates. Students also measure their heights and weights in order to calculate their BMIs (body mass index). Then they use the collected data to create and analyze scatterplots of the different variables to determine if any relationships exist between the measured variables. Discussions about the trends observed and possible health concerns conclude the activity.