Math / 8th Grade / Unit 8: Bivariate Data

Bivariate Data

Students combine their knowledge of linear functions with knowledge of data representations and analysis to make the jump from univariate data in one variable to bivariate data in two variables.

Math

Unit 8

8th Grade

Unit Summary

Please Note: In March 2026, this unit and its lesson plans received a round of enhancements. Teachers should pay close attention as they intellectually prepare to account for the updated pacing and content.

In Unit 8, 8th Grade students extend their understanding of statistics from univariate data to bivariate data. They re-engage in the major work of the grade, analyzing scatterplots for positive, negative, linear, and nonlinear trends, drawing lines to represent relationships, writing and interpreting linear equations, and using those equations to make predictions beyond the scope of the data MP.7 Look for and make use of structure. MP.2 Reason abstractly and quantitatively. . Students also analyze categorical bivariate data using two-way tables, calculate relative frequencies, and determine whether there is evidence of an association between variables.

Throughout the unit, students examine data representations and ask whether patterns suggest an association and what that association means in context. They distinguish between association and causation and justify their conclusions using proportional reasoning and structure in the data. MP.3 Construct viable arguments and critique the reasoning of others.

Prior to 8th Grade, students explored how and why data is collected—by thinking about statistical questions, samples, populations, and various ways to analyze data representations. Students worked with line plots, histograms, and box plots, and they considered what the shape, center, and spread of these data sets said about the data itself.

In High School, students’ understanding of statistics is formalized. They analyze bivariate data using functions, design and carry out experiments, and make predictions about outcomes based on probabilities. Students use their knowledge of association between variables as a basis for correlation. They develop nonlinear models for data and formally analyze how closely the model fits the data.

Pacing: 12 instructional days (9 lessons, 2 flex days, 1 assessment day)

Assessment

The following assessments accompany Unit 8.

Pre-Unit

Have students complete the Pre-Unit Assessment and Pre-Unit Student Self-Assessment before starting the unit. Use the Pre-Unit Assessment Analysis Guide to identify gaps in foundational understanding and map out a plan for learning acceleration throughout the unit.

Mid-Unit

Have students complete the Mid-Unit Assessment.

Post-Unit

Use the resources below to assess student understanding of the unit content and action plan for future units.

Expanded Assessment Package

Use student data to drive instruction with an expanded suite of assessments. Unlock Pre-Unit and Mid-Unit Assessments, and detailed Assessment Analysis Guides to help assess foundational skills, progress with unit content, and help inform your planning.

Unit Prep

Intellectual Prep

Suggestions for how to prepare to teach this unit

Internalization of Standards via the Post-Unit Assessment

Take the Post-Unit Assessment. Annotate for:
- Standards that each question aligns to
- Strategies and representations used in daily lessons
- Relationship to Essential Understandings of unit
- Lesson(s) that Assessment points to

Internalization of Trajectory of Unit

Read and annotate the Unit Summary.
Notice the progression of concepts through the unit using the Lesson Map.
Do all Target Tasks. Annotate the Target Tasks for:
- Essential Understandings
- Connection to Post-Unit Assessment questions
Identify key opportunities to engage students in academic discourse. Read through our Teacher Tool on Academic Discourse and refer back to it throughout the unit.

Unit-Specific Intellectual Prep

Read the Progressions for the Common Core State Standards in Mathematics, 6-8 Statistics and Probability for standards relevant to this unit, starting on page 185.
Read the table below that includes models used throughout the unit.

Model	Example
scatter plot
two-way table

Essential Understandings

The central mathematical concepts that students will come to understand in this unit

Patterns of association may exist between two variables defined in bivariate data. Scatter plots and two-way tables reveal these associations and support interpretation of relationships.
Relationships between variables in bivariate data can take several forms—positive or negative, linear or nonlinear— and in categorical data, associations are examined through relative frequencies and proportional reasoning.
Knowing how two variables are associated allows us to make informed predictions about data beyond what is directly observed.
For quantitative bivariate data that show a linear association, a line can be used to model the relationship and represent the trend in the data.

Materials

The materials, representations, and tools teachers and students will need for this unit

Calculators (1 per student)
Graph Paper (1-2 sheets per student)
Wikisticks or raw spaghetti (1 per student or small group)
Scatter Plots
Scatter Plot Relationships Sorting Cards (1 per group) — print single-sided
Completing Two-Way Tables with Info Gap Cards (1 per pair of students) — print single-sided

To see all the materials needed for this course, view our 8th Grade Course Material Overview.

Vocabulary and Models

Unit Vocabulary

bivariate data

line fit to data (trendline)

linear/nonlinear association

positive/negative association

relative frequency

scatter plot

trend

Foundational Vocabulary

cluster

outlier

To see all the vocabulary for Unit 8, view our 8th Grade Vocabulary Glossary.

Access foundational vocabulary for Unit 8 in the same 8th Grade Vocabulary Glossary.

Lesson Map

Topic A: Associations in Bivariate Numerical Data

Lesson

Define bivariate data. Analyze data in scatter plots.

Standards

8.SP.A.1

Lesson

Create scatter plots for data sets and make observations about the data.

Standards

8.SP.A.1

Lesson

Identify and describe associations in scatter plots including linear/nonlinear associations, positive/negative associations, clusters, and outliers.

Standards

8.SP.A.1

Lesson

Informally fit a line to data. Judge the fit of the line and make predictions about the data based on the line.

Standards

8.SP.A.2

Lesson

Write equations to represent lines fit to data and make predictions based on the line.

Standards

8.SP.A.3

Lesson

Interpret the slope and $$y$$-intercept of a fitted line in context.

Standards

8.SP.A.3

Topic B: Associations in Bivariate Categorical Data

Lesson

Create and analyze two-way tables representing bivariate categorical data.

Standards

8.SP.A.4

Lesson

Calculate relative frequencies in two-way tables to investigate associations in data.

Standards

8.SP.A.4

Lesson

Complete two-way tables and identify associations in the data.

Standards

8.SP.A.4

Common Core Standards

Key

Major Cluster

Supporting Cluster

Additional Cluster

Core Standards

The content standards covered in this unit

Statistics and Probability

8.SP.A.1 — Construct 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.

Statistics and Probability

8.SP.A.1 — Construct 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.A.2 — Know 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 data points to the line.

Statistics and Probability

8.SP.A.2 — Know 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 data points to the line.
8.SP.A.3 — Use the equation of a linear model to solve problems in the context of bivariate measurement data, interpreting the slope and intercept. For example, in a linear model for a biology experiment, interpret a slope of 1.5 cm/hr as meaning that an additional hour of sunlight each day is associated with an additional 1.5 cm in mature plant height.

Statistics and Probability

8.SP.A.3 — Use the equation of a linear model to solve problems in the context of bivariate measurement data, interpreting the slope and intercept. For example, in a linear model for a biology experiment, interpret a slope of 1.5 cm/hr as meaning that an additional hour of sunlight each day is associated with an additional 1.5 cm in mature plant height.
8.SP.A.4 — Understand that patterns of association can also be seen in bivariate categorical data by displaying frequencies and relative frequencies in a two-way table. Construct and interpret a two-way table summarizing data on two categorical variables collected from the same subjects. Use relative frequencies calculated for rows or columns to describe possible association between the two variables. For example, collect data from students in your class on whether or not they have a curfew on school nights and whether or not they have assigned chores at home. Is there evidence that those who have a curfew also tend to have chores?

Statistics and Probability

8.SP.A.4 — Understand that patterns of association can also be seen in bivariate categorical data by displaying frequencies and relative frequencies in a two-way table. Construct and interpret a two-way table summarizing data on two categorical variables collected from the same subjects. Use relative frequencies calculated for rows or columns to describe possible association between the two variables. For example, collect data from students in your class on whether or not they have a curfew on school nights and whether or not they have assigned chores at home. Is there evidence that those who have a curfew also tend to have chores?

Foundational Standards

Standards covered in previous units or grades that are important background for the current unit

Functions

8.F.A.3

Functions

8.F.A.3 — Interpret the equation y = mx + b as defining a linear function, whose graph is a straight line; give examples of functions that are not linear. For example, the function A = s² giving the area of a square as a function of its side length is not linear because its graph contains the points (1,1), (2,4) and (3,9), which are not on a straight line.
8.F.B.4

Functions

8.F.B.4 — Construct a function to model a linear relationship between two quantities. Determine the rate of change and initial value of the function from a description of a relationship or from two (x, y) values, including reading these from a table or from a graph. Interpret the rate of change and initial value of a linear function in terms of the situation it models, and in terms of its graph or a table of values.
8.F.B.5

Functions

8.F.B.5 — Describe qualitatively the functional relationship between two quantities by analyzing a graph (e.g., where the function is increasing or decreasing, linear or nonlinear). Sketch a graph that exhibits the qualitative features of a function that has been described verbally.

Ratios and Proportional Relationships

6.RP.A.3.C

Ratios and Proportional Relationships

6.RP.A.3.C — Find a percent of a quantity as a rate per 100 (e.g., 30% of a quantity means 30/100 times the quantity); solve problems involving finding the whole, given a part and the percent.
7.RP.A.3

Ratios and Proportional Relationships

7.RP.A.3 — Use proportional relationships to solve multistep ratio and percent problems. Examples: simple interest, tax, markups and markdowns, gratuities and commissions, fees, percent increase and decrease, percent error.

Statistics and Probability

6.SP.A.2

Statistics and Probability

6.SP.A.2 — Understand that a set of data collected to answer a statistical question has a distribution which can be described by its center, spread, and overall shape.
6.SP.B.4

Statistics and Probability

6.SP.B.4 — Display numerical data in plots on a number line, including dot plots, histograms, and box plots.
7.SP.A.1

Statistics and Probability

7.SP.A.1 — Understand that statistics can be used to gain information about a population by examining a sample of the population; generalizations about a population from a sample are valid only if the sample is representative of that population. Understand that random sampling tends to produce representative samples and support valid inferences.
7.SP.A.2

Statistics and Probability

7.SP.A.2 — Use data from a random sample to draw inferences about a population with an unknown characteristic of interest. Generate multiple samples (or simulated samples) of the same size to gauge the variation in estimates or predictions. For example, estimate the mean word length in a book by randomly sampling words from the book; predict the winner of a school election based on randomly sampled survey data. Gauge how far off the estimate or prediction might be.
7.SP.C.5

Statistics and Probability

7.SP.C.5 — Understand that the probability of a chance event is a number between 0 and 1 that expresses the likelihood of the event occurring. Larger numbers indicate greater likelihood. A probability near 0 indicates an unlikely event, a probability around 1/2 indicates an event that is neither unlikely nor likely, and a probability near 1 indicates a likely event.

Future Standards

Standards in future grades or units that connect to the content in this unit

High School — Statistics and Probability

S.ID.B.5

High School — Statistics and Probability

S.ID.B.5 — Summarize categorical data for two categories in two-way frequency tables. Interpret relative frequencies in the context of the data (including joint, marginal, and conditional relative frequencies). Recognize possible associations and trends in the data.
S.ID.B.6

High School — Statistics and Probability

S.ID.B.6 — Represent data on two quantitative variables on a scatter plot, and describe how the variables are related.
S.ID.C.7

High School — Statistics and Probability

S.ID.C.7 — Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data.
S.ID.C.8

High School — Statistics and Probability

S.ID.C.8 — Compute (using technology) and interpret the correlation coefficient of a linear fit.
S.ID.C.9

High School — Statistics and Probability

S.ID.C.9 — Distinguish between correlation and causation.

Standards for Mathematical Practice

CCSS.MATH.PRACTICE.MP1 — Make sense of problems and persevere in solving them.
CCSS.MATH.PRACTICE.MP2 — Reason abstractly and quantitatively.
CCSS.MATH.PRACTICE.MP3 — Construct viable arguments and critique the reasoning of others.
CCSS.MATH.PRACTICE.MP4 — Model with mathematics.
CCSS.MATH.PRACTICE.MP5 — Use appropriate tools strategically.
CCSS.MATH.PRACTICE.MP6 — Attend to precision.
CCSS.MATH.PRACTICE.MP7 — Look for and make use of structure.
CCSS.MATH.PRACTICE.MP8 — Look for and express regularity in repeated reasoning.

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