Data gathering - Linear regressions
In groups of three, students gather data by experiment or observation in one of nine activities. Each group models the data they gathered, creates a display, and presents results to the class using an overhead projector.
A lesson plan for grades 8–12 Mathematics
Learning outcomes
- Students will be able to gather at least ten sets of data, use an automatic grapher to graph the sets into a scatter plot, determine the line and equation of best fit, and use the results to make predictions.
- Algebra I students will estimate a line of best fit instead of determining the linear equation using an automatic grapher.
Teacher planning
Time required for lesson
90 minutes
Materials/resources
- string and weight pendulum
- stopwatch
- twelve different sized jar lids
- a bag of beans
- three ping pong balls
- thirty dice
- rulers, metersticks or yard sticks
- graph paper, colored pencils
- list of data gathering activities cut into separate activities
- tape measure
- grid paper
- overhead pens and transparency sheets
Technology resources
automatic grapher (optional for Algebra I), overhead projector (or other presentation device)
Pre-activities
For most classes: The student must have already practiced entering data into an automatic grapher and using the result to find the line and equation of best fit. (Optional for Algebra I)
Algebra I students will have practiced graphing bivariate data sets and drawing in an estimated line of best fit.
Activities
- Review the procedure for using the automatic grapher to find the line and equation of best fit.
- Divide the students into groups of three.
- Each group will randomly pick an activity from the previously prepared list (see Supplemental Information).
- Each group will read its activity and then go to the area where all the materials have been laid out and pick up the equipment that is needed to gather the data. One member of the group will perform the activity while one the others measures the outcomes and the last member of the group records the data.
- The group will record the results on a transparency with a Cartesian Coordinate Plain copied on the upper half. The graph must include:
- a descriptive title
- properly numbered scale on the axes
- clearly labeled axes
- scatter plot of the data points
- regression line (estimated for Algebra I classes)
- three regression points
The results recorded below the graph will include:
- ten data points
- slope of the line of best fit
- y-intercept of the line of best fit
- equation of the line of best fit
- correlation coefficient r (this is not covered in most Algebra I and Algebra II classes…teacher could substitute “type of association: positive or negative” and “strength of association: strong, moderate, or weak”)
- type of regression (this is not covered in Algebra I, where all regressions would be linear)
- one extrapolated and one interpolated prediction and a statement on the reliability of each (optional in Algebra I and Algebra II)
Assessment
Each group will present its data results and findings to the class using the overhead transparencies on which the results were recorded.
Supplemental information
Data Gathering Activities
- Students measure height and foot length (no shoes). Use at least ten sets of data. (Equipment: Measuring device)
- Students drop a ball from various heights and measure the height of the first bounce. A tape measure hung vertically and a wall will improve accuracy. As the record ordered pairs (drop height, height of rebound), students will probably have quite a bit of experimental error. This can be minimized by having two or three students sight the rebound height and averaging their sightings. Use at least ten sets of data. (Equipment: Tape measure, ping pong ball)
- Students measure the diameter and circumference of empty cans or jar lids. When you plot diameter on the horizontal axis then the slope or their linear model will be ?? Use at least ten sets of data. (Equipment: cans/lids, tape measure)
- The size of the image made by an overhead projector changes as the distance between the projector and screen changes. Students gather ordered pairs of the form (distance from screen, length of projected image). You must be sure to focus the image at each distance.Use at least ten sets of data. (Equipment:overhead projector, measuring device, image on the screen)
- Students will count the number of dry beans that will fill a jar lid without any overlapping. They will also measure the diameter of each jar lid. The number of beans that will fit in each lid provides a relative measure of its area. Use at least ten sets of data. (Equipment: Jar lids, dry beans, ruler)
- Students will vary the length of the string and measure how the period of the pendulum (time to complete one swing across and back) changes. To measure the period, students should hold one end of the string stable, pull the weight to one side, let the weight make ten complete swings, record the time, and then divide the time by 10. Ordered pairs (Length of string, period) should be recorded at about 10 cm intervals. Use at least ten sets of data. (Equipment: pendulum, string (about 1 meter), measuring device, stopwatch)
- Roll all the dice: after each roll remove every die that is showing a 6. Continue rolling until all dice have been removed. Record ordered pairs of the form (number of roll, number of remaining dice). (Equipment: dice)
- Draw different sizes of equilateral triangles with bases along lines of grid paper. Students should measure the size of the base using grid lengths and count squares to estimate the area of each triangle. Use at least ten sets of data. (Equipment: grid paper, rulers)
- Students measure the height of a car and the height of it’s tires. Use at least ten sets of data. (Equipment: measuring device)
North Carolina curriculum alignment
Mathematics (2004)
Grade 9–12 — Advanced Functions and Modeling
- Goal 1: Data Analysis and Probability - The learner will analyze data and apply probability concepts to solve problems.
- Objective 1.01: Create and use calculator-generated models of linear, polynomial, exponential, trigonometric, power, and logarithmic functions of bivariate data to solve problems.
- Interpret the constants, coefficients, and bases in the context of the data.
- Check models for goodness-of-fit; use the most appropriate model to draw conclusions and make predictions.
- Objective 1.01: Create and use calculator-generated models of linear, polynomial, exponential, trigonometric, power, and logarithmic functions of bivariate data to solve problems.
Grade 9–12 — Advanced Placement Statistics
- Goal 4: Algebra - The learner will analyze bivariate data to solve problems.
- Objective 4.01: Analyze bivariate data.
- Recognize and analyze correlation and linearity.
- Determine the least squares regression line.
- Create residual plots and identify outliers and influential points to analyze data.
- Use logarithmic and power transformations to analyze data.
- Objective 4.01: Analyze bivariate data.
Grade 9–12 — Algebra 1
- Goal 3: Data Analysis and Probability - The learner will collect, organize, and interpret data with matrices and linear models to solve problems.
- Objective 3.03: Create linear models for sets of data to solve problems.
- Interpret constants and coefficients in the context of the data.
- Check the model for goodness-of-fit and use the model, where appropriate, to draw conclusions or make predictions.
- Objective 3.03: Create linear models for sets of data to solve problems.
Grade 9–12 — Algebra 2
- Goal 2: Algebra - The learner will use relations and functions to solve problems.
- Objective 2.04: Create and use best-fit mathematical models of linear, exponential, and quadratic functions to solve problems involving sets of data.
- Interpret the constants, coefficients, and bases in the context of the data.
- Check the model for goodness-of-fit and use the model, where appropriate, to draw conclusions or make predictions.
- Objective 2.04: Create and use best-fit mathematical models of linear, exponential, and quadratic functions to solve problems involving sets of data.
Grade 9–12 — Integrated Mathematics 4
- Goal 3: Data Analysis and Probability - The learner will analyze data to solve problems.
- Objective 3.02: Create and use calculator-generated models of linear, polynomial, exponential, trigonometric, power, logistic, and logarithmic functions of bivariate data to solve problems.
- Interpret the constants, coefficients, and bases in the context of the data.
- Check models for goodness-of-fit; use the most appropriate model to draw conclusions or make predictions.
- Objective 3.02: Create and use calculator-generated models of linear, polynomial, exponential, trigonometric, power, logistic, and logarithmic functions of bivariate data to solve problems.
Grade 9–12 — Pre-Calculus
- Goal 2: Algebra - The learner will use relations and functions to solve problems.
- Objective 2.03: For sets of data, create and use calculator-generated models of linear, polynomial, exponential, trigonometric, power, logistic, and logarithmic functions.
- Interpret the constants, coefficients, and bases in the context of the data.
- Check models for goodness-of-fit; use the most appropriate model to draw conclusions or make predictions.
- Objective 2.03: For sets of data, create and use calculator-generated models of linear, polynomial, exponential, trigonometric, power, logistic, and logarithmic functions.
- Common Core State Standards
- Mathematics (2010)
Grade 8
- Statistics & Probability
- 8.SP.1Construct and interpret scatter plots for bivariate measurement data to investigate patterns of association between two quantities. Describe patterns such as clustering, outliers, positive or negative association, linear association, and nonlinear association.
- 8.SP.2Know that straight lines are widely used to model relationships between two quantitative variables. For scatter plots that suggest a linear association, informally fit a straight line, and informally assess the model fit by judging the closeness of the...
- Statistics & Probability
High School: Statistics & Probability
- Interpreting Categorical & Quantitative Data
- SP.ICQ.6Represent data on two quantitative variables on a scatter plot, and describe how the variables are related. Fit a function to the data; use functions fitted to data to solve problems in the context of the data. Use given functions or choose a function...
- SP.ICQ.8Compute (using technology) and interpret the correlation coefficient of a linear fit.
- Interpreting Categorical & Quantitative Data
- Mathematics (2010)






