Finding the Line of Best Fit from Temperature Data
Algebra · Algebra 1 · Big Ideas Math · Ron Larson · Chapter 4-
Step-by-step video solution
Watch video solutionThis DoAssignment.ca lesson explains the problem with a narrated, step-by-step solution for students studying Algebra from Algebra 1 · Big Ideas Math · Ron Larson.
Problem and lesson overview
Question:
A weather station records the daily high temperature in degrees Fahrenheit and the number of ice cream cones sold at a nearby shop over 6 days. The data points are: 68 comma 42, 72 comma 58, 75 comma 71, 78 comma 85, 82 comma 103, and 85 comma 118. Create a scatter plot of the data, identify the correlation, and find the equation of the line of best fit using two reasonable points from the data. Then predict how many ice cream cones would be sold if the high temperature reaches 90 degrees Fahrenheit.
Finding the Line of Best Fit from Temperature Data
This lesson explores how to create a scatter plot from a two-variable data set, identify the type and strength of correlation, and write the equation of the line of best fit using two representative points. Students then use the equation to make predictions beyond the recorded data.
In this lesson, you will learn to:
• Plot two-
A weather station records the daily high temperature in degrees Fahrenheit and the number of ice cream cones sold at a nearby shop over 6 days. The data points are: 68 comma 42, 72 comma 58, 75 comma 71, 78 comma 85, 82 comma 103, and 85 comma 118. Create a scatter plot of the data, identify the correlation, and find the equation of the line of best fit using two reasonable points from the data. Then predict how many ice cream cones would be sold if the high temperature reaches 90 degrees Fahrenheit.
Finding the Line of Best Fit from Temperature Data
This lesson explores how to create a scatter plot from a two-variable data set, identify the type and strength of correlation, and write the equation of the line of best fit using two representative points. Students then use the equation to make predictions beyond the recorded data.
In this lesson, you will learn to:
• Plot two-
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