Standard #: MA.912.DP.2.4

Benchmark Instructional Guide

Connecting Benchmarks/Horizontal Alignment

• MA.912.DP.1.1
• MA.912.DP.1.2
• MA.912.DP.1.3

 

Terms from the K-12 Glossary

• Numerical Data 
• Line of Fit 
• Scatter Plot

 

Vertical Alignment

Previous Benchmarks

• MA.8.DP.1

Next Benchmarks

• MA.912.DP.2.7
• MA.912.DP.2.8
• MA.912.DP.2.9

 

Purpose and Instructional Strategies

In grade 8, students first worked with scatter plots and lines of fit. In Algebra I, students relate the slope and $y$-intercept of a line of fit to association in bivariate numerical data and interpret these features in real-world contexts. In later courses, students use the correlation coefficient to measure how well a line fits the data in a scatter plot, and they also work with scatter plots that suggest quadratic and exponential models. 

• This is an extension of MA.912.DP.1.1, where students are working with numerical bivariate data (scatter plots and line graphs). It is good to review with students that a scatter plot is a display of numerical data sets between two variables. 
  • They are good for showing a relationship or association between two variables. 
  • They can reveal trends, shape of trend or strength of relationship trend. 
  • They are useful for highlighting outliers and understanding the distribution of data. 
  • One variable could be the progression of time, like in a line graph. 
• In this benchmark, students are fitting a linear function to numerical bivariate data, interpreting the slope and $y$-intercept based on the context and using that linear function to make predictions about values that correspond to parts of the graph that lie beyond or within the scatter plot. 
• Instruction includes the use of technology for students to understand the difference between a line of fit and a line of best fit. Additionally, instruction of this benchmark should be combined with MA.912.DP.2.6 and MA.912.DP.2.5, as these are extensions of this benchmark. 
• During instruction is important to distinguish the difference between a “line of fit” and the “line of best fit.” 
  • A “line of fit” is used when students are visually investigating numerical bivariate data that appears to have a linear relationship and can sketch a line (using a writing instrument and straightedge) that appears to “fit” the data. Using this “line of fit” students can estimate its slope and $y$-intercept and use that information to interpret the context of the data.
  • The “line of best fit” (also referred to as a “trend line”) is used when the data is further analyzed using linear regression calculations (the process of minimizing the squared distances from the individual data values to the line), often done with the assistance of technology.

 

Common Misconceptions or Errors

• Students may not know how to sketch a line of fit. 
  • For example, they may always go through the first and last points of data. 
• Students may be confuse the two variables when interpreting the data as related to the context. 
• Students may not know the difference between interpolation (predictions within a data set) and extrapolation (predictions beyond a data set).

 

Strategies to Support Tiered Instruction

• Teacher provides sketched lines of fit and has students identify the one that best models the data. 
• Teacher provides a sentence frame for interpreting the data in the context of the problem using two different colored highlighters to highlight the same variable in the sentence frame and table or graph. 
  • Example: 
    
    
    

• Teacher models creating a scatter plot on a piece of graph paper, then has students place a piece of spaghetti on the scatter plot to model the line of best fit. Students could also use a coordinate plane peg board to plot each point creating a scatter plot and then use a rubber band to model the line of best fit. 
• Instruction includes vocabulary development by co-creating a graphic organizer for interpolation and extrapolation.

 

Instructional Tasks

Instructional Task 1 (MTR.4.1, MTR.5.1, MTR.6.1) 

*The strategies, tasks and items included in the B1G-M are examples and should not be considered comprehensive.