Baby Sizes 2 Copyright © 2026 Pearson Education, Inc. Baby Sizes (50 – 60 minutes) Learning Objective(s): Students will fit a linear function to bivariate numerical data. Students will interpret the slope and y-intercept of a linear model. Students will interpolate and extrapolate data to make predictions based on a linear model. Material needed: Student pages: Baby Sizes Calculator or online calculator Graph paper Lesson Procedure: Warm–Up 10 minutes Prompt: What are some situations where you might encounter bivariate data? Discuss: Have students share with the group examples of situations that would involve bivariate data. Some examples might include inches of precipitation vs. water levels in the river basin, size of animal vs. amount of food it eats, or number of people on a train vs. how quickly the train leaves the station. Guided Instruction 15 minutes Present: scenario for Big Baby Example: Why is it important to conduct research into newborn babies? Sample answer: The research may improve the health and development of future children. How can you look for correlations in data? Sample answer: I can plot a scatterplot and look for the overall shape of the dots. How do you find the line of best fit for a set of data? Sample answer: Use a ruler to draw a line that passes as close to as many points as possible. Review: key terms – line of best fit, model, scatter plot line of best fit: a straight line on a scatter plot that passes as close to as many points as possible model: a mathematical representation of a set of data scatterplot: a graphical representation of bivariate data where the x- and y-coordinates of each point represent the two variables in the data Independent Practice 20 minutes Distribute: student activity Big Baby. Have students independently fit the data to a linear function. Then, have them work with a partner to answer questions 2–4. Closure 10–15 minutes Review Answers: 1. See graph. 2. Sample answer: 0.1853 kilograms per centimeter; The slope of the line of best fit shows the rate at which mass increases per unit length. 3. Sample answers: a. –6.023; b. The y-intercept is negative, which does not make sense because a baby cannot have negative mass. 4. a. more; b. more 5. a. 1.389 kg; b. 3.242 kg; c. 5.095 kg Discuss: How can you use bivariate data to make predictions? How accurate do you think these predictions will be?
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