Standard 1: Represent and investigate numerical bivariate data.

Students are given a scatter plot in a real-world context and asked to describe the association between the variables.

Type: Formative Assessment

Students are given a scatterplot in a real-world context and asked to describe the association between the variables.

Type: Formative Assessment

Students are given a scatterplot in a real-world context and asked to describe the association between the variables.

Type: Formative Assessment

Students are given a scatterplot in a real-world context and asked to describe the association between the variables.

Type: Formative Assessment

Students are given a set of data and are asked to choose the scale for the axes, graph the data, and explain why they chose the scales they used.

Type: Formative Assessment

Students are asked to describe potentially important variables that can be used in a model to predict the amount of time required to get to school.

Type: Formative Assessment

Students are asked to describe potentially important variables that can be used in a model to predict the amount of damage caused by a thunderstorm.

Type: Formative Assessment

Students are asked to construct a scatterplot corresponding to a given set of data.

Type: Formative Assessment

Students are asked to compare two lines fitted to data to determine which fit is better.

Type: Formative Assessment

Students are asked to informally assess three lines fitted to data to determine which fit is the best.

Type: Formative Assessment

Students are asked to informally fit a line to model the relationship between two quantitative variables and to assess how well that line fits the data.

Type: Formative Assessment

Students are asked to informally fit a line to model the relationship between two quantitative variables and to assess how well that line fits the data.

Type: Formative Assessment

This lesson is an introductory lesson to correlation coefficients. Students will engage in research prior to the teacher giving any direct instruction. The teacher will provide instruction on how to find the correlation coefficient by hand and using Excel.

Type: Lesson Plan

This lesson is an introductory lesson to correlation coefficients. Students will engage in research prior to the teacher giving any direct instruction. The teacher will provide instruction on how to find the correlation coefficient by hand and using Excel.

Type: Lesson Plan

Students will explore trends in voter turnout during the early 20th century, create a line graph to represent voter turnout data, and make comparisons to draw conclusions about the impact of the 19th Amendment, in this integrated lesson plan.

Type: Lesson Plan

In this lesson plan, students will create and compare a scatterplot and line graph to determine which is the most appropriate representation of voter turnout and voting age population data from past presidential elections. Students will use both graphs to explore how the Reconstruction Amendments broadened the opportunity for civic participation.

Type: Lesson Plan

Students will participate in collecting trash either on campus or another location. They will compare the distance traveled and the weight of the trash bag collected. Students will explore the use of mean and median in finding the ratios of the data set. They will discuss the use of mean and median in finding the relationship between the independent and dependent variables. Students will examine their scatter plot and determine if any patterns of association exist. They will compare their data to a coastal cleanup report. Finally, students will use the data to help determine interventions at the local, state and national level regarding environmental issues.

Type: Lesson Plan

Students investigate the amount of space that could be saved by flattening cardboard boxes. The analysis includes linear graphs and regression analysis along with discussions of slope and a direct variation phenomenon.

Type: Lesson Plan

In this activity, students investigate a set of bivariate data to determine if there is a relationship between concession sales in the park and temperature. Students will construct a scatter plot, model the relationship with a linear function, write the equation of the function, and use it to make predictions about values of variables.

Type: Lesson Plan

Students create scatter plots, calculate a regression equation using technology, and interpret the slope and y-intercept of the equation in the context of the data. This review lesson relates graphical and algebraic representations of bivariate data.

Type: Lesson Plan

The students will use NBA player data to determine if there is a correlation between the height of a basketball player and his free throw percentage. The students will use technology to create scatter plots, find the regression line and calculate the correlation coefficient.

Basketball is a tall man's sport in most regards. Shooting, rebounding, blocking shots - the taller player seems to have the advantage. But is that still true when shooting free throws?

Type: Lesson Plan

This lesson is an introduction to scatterplots and how to use a trend line to make predictions. Students should have some knowledge of graphing bivariate data prior to this lesson.

Type: Lesson Plan

Students will gather and use data to calculate a line of fit and the correlation coefficient with their classmates' height and hand size. They will use their line of fit to make approximations.

Type: Lesson Plan

Who doesn't want to save money? In this lesson, students will learn how a better credit score will save them money. They will use a scatter plot to see the relationship between credit scores and car loan interest rates. They will determine a line of fit equation and interpret the slope and y-intercept to make conclusions about interest and credit scores.

Type: Lesson Plan

Students will calculate the residuals of two-variable data. Teachers are provided with materials to review, present, practice, and assess students for this new topic. This is an introductory lesson and could be used before teaching residual plots.

Type: Lesson Plan

This lesson provides the students with scatter plots, lines of best fit and the linear equations to practice interpreting the slope and y-intercept in the context of the problem.

Type: Lesson Plan

Students will enjoy this project lesson that allows them to choose and collect their own data. They will create a scatter plot and find the line of fit. Next they write interpretations of their slope and y-intercept. Their final challenge is to calculate residuals and conclude whether or not their data is consistent with their linear model.

Type: Lesson Plan

Students will make a scatter plot and then create a line of fit for the data. From their graph, students will make predictions and describe relationships between the variables. Students will make predictions, inquire, and formulate ideas from observations and discussions.

Type: Lesson Plan

This lesson examines the differences between quantitative and qualitative data and guides students through displaying quantitative data on a scatter plot and then separating the data into qualitative categories to be displayed and interpreted in a two-way frequency table.

Type: Lesson Plan

In this lesson, students plot temperature and luminosity data from a provided star table to create a scatter plot. They will analyze the data to sequence the colors of stars from hottest to coolest and to describe the relationship between temperature and luminosity. This lesson does not address differentiation between absolute and apparent magnitude.

Type: Lesson Plan

Students create scatter plots, and lines of fit, and then calculate the correlation coefficient. Students analyze the results and make predictions. This lesson includes step-by-step directions for calculating the correlation coefficient using Excel, GeoGebra, and a TI-84 Plus graphing calculator. Students will make predictions for the number of views of a video for any given number of weeks on the charts.

Type: Lesson Plan

Text Count and Homework Minutes

The Students will engage in counting Text minutes and Homework minutes. They will keep a journal to be kept over a four day period, Monday thru Thursday. The assignment can be done on Friday. The students will record the number of text messages received and sent out during the hours of 3:00-11:00. On the second journal the students will also record the number of minutes spent on homework between the hours of 3:00-11:00. They will compile this information into graphs on that 5^th day Friday. With this data the students will be able to plot a scatter plot graph and learn how to recognize patterns on a scatter plot graph. Students will see the relevance of a correlation even if the graph is not linear or a straight line. Students will identify trends and articulate the reason for the trends.

Type: Lesson Plan

Through a slide show presentation, students will be led through data in one variable, bivariate data, scatter plots, lines of best fit, outliers, and correlations. They will be able to analyze bivariate data to determine correlations, associations, and the impact of outliers.

Type: Lesson Plan

This lesson uses real-world examples to practice interpreting the slope and y-intercept of a linear model in the context of data. Students will collect data, graph a scatter plot, and use spaghetti to identify a line of fit. A PowerPoint is included for guidance throughout the lesson and guided notes are also provided for students.

Type: Lesson Plan

In this lesson students learn how to informally determine a "best fit" line for a scatter plot by considering the idea of closeness.

Type: Lesson Plan

This lesson is an introductory lesson to scatter plots and line of best fit (trend lines). Students will be using small round candy pieces to represent different associations in scatter plots and measure each other's height and arm span to create their own bivariate data to analyze. Students will be describing the association of the data, patterns of the data, informally draw a line of best fit (trend line), write the equation of the trend line, interpret the slope and y-intercept, and make predictions.

Type: Lesson Plan

Students use real-world data to construct and interpret scatter plots using technology. Students will create a scatter plot with a line of fit and a function. They describe the relationship of bivariate data. They recognize and interpret the slope and y-intercept of the line of fit within the context of the data.

Type: Lesson Plan

Students will learn how the correlation coefficient is used to determine the strength of relationships among real data. Students use card sorting to order situations from negative to positive correlations. Students will create a scatter plot and use technology to calculate the line of fit and the correlation coefficient. Students will make a prediction and then use the line of fit and the correlation coefficient to confirm or deny their prediction.

Students will learn how to use the Linear Regression feature of a graphing calculator to determine the line of fit and the correlation coefficient.

The lesson includes the guided card sorting task, a formative assessment, and a summative assessment.

Type: Lesson Plan

This lesson consists of using data to make scatter plots, identify the line of fit, write its equation, and then interpret the slope and the y-intercept in context. Students will also use the line of fit to make predictions.

Type: Lesson Plan

This lesson provides activities for students to conceptually understand how to gather, organize, and interpret data using a scatter plot. They will be required to work cooperatively to complete certain tasks. They will use estimation strategies to complete a experiment that compares the the length of their hand to the number of centimeter cubes they can grab in order to make predictions about data with a larger sample size. Students will be assessed formally and informally throughout lesson via slide notes and data collection tool.

Type: Lesson Plan

Students will do an exploration activity with paper airplanes. They will be up and moving around. Students will be able to understand and analyze bivariate data that they will be able to plot from the exploration activity. They will be able to describe patterns, and also find facts and come up with conclusions based on the data they gather.

Type: Lesson Plan

Students will use GeoGebra software to explore the concept of correlation coefficient in graphical images of scatter plots. They will also learn about numerical and qualitative aspects of the correlation coefficient, and then do a matching activity to connect all these representations of the correlation coefficient. They will use an interactive program file in GeoGebra to manipulate the points to create a certain correlation coefficient. Step-by-step instructions are included to create the graph in GeoGebra and calculate the r correlation coefficient.

Type: Lesson Plan

Students will analyze data to create scatter plots. They will draw the line of best fit to determine linear models. The teacher will use PowerPoint and activities included to guide the students into finding the line of best fit.

Type: Lesson Plan

Students will graph, describe, and determine patterns of associations for bivariate data. This lesson incorporates vocabulary development and collaboration.

Type: Lesson Plan

In this lesson, students will learn how to analyze data and find the equation of the line of best fit. Students will then find the slope and intercept of the best fit line and interpret the meaning in the context of the data.

Type: Lesson Plan

This lesson will get your student up to speed with constructing and interpreting scatter plots using bivariate data. With this lesson, patterns of association are investigated between two quantities and positive or negative association as well as linear and non-linear association is determined through the use of data from the NFL as reported by ESPN. Data from Consumer Reports is used to determine associations between shoe price and quality.

Type: Lesson Plan

Students will sketch and interpret the line of fit and then describe the correlation of the data. Students will determine if there’s a correlation between foot size and height by collecting data.

Type: Lesson Plan

Students will graph scatterplots and draw a line of fit. Next, students will write an equation for the line and use it to interpret the slope and y-intercept in context. Students will also use the graph and the equation to make predictions.

Type: Lesson Plan

Students will apply skills (making a scatter plot, finding Line of Best Fit, finding an equation and predicting the y-value of a point on the line given its x-coordinate) to a fuel efficiency problem and then consider other factors such as color, style, and horsepower when designing a new coupe vehicle.

Model Eliciting Activities, MEAs, are open-ended, interdisciplinary problem-solving activities that are meant to reveal students’ thinking about the concepts embedded in realistic situations. Click here to learn more about MEAs and how they can transform your classroom.

Type: Lesson Plan

In this lesson, students will graph a scatter plot and learn how to recognize patterns. The students will learn that correlation may still exist even though the points are not in a perfectly straight line (linear function). Students will be able to identify outliers, describe associations, and justify their reasoning.

Type: Lesson Plan

This lesson is an introductory lesson to correlation coefficients. Students will engage in research prior to the teacher giving any direct instruction. The teacher will provide instruction on how to find the correlation coefficient by hand and using Excel.

Type: Lesson Plan

In this activity, students use scatter plots to compare the estimated and actual heights of familiar celebrities and athletes. They will determine how their answers impact the correlation of their data, including the influence of outliers. Finally, they will compare their correlation to that provided in a scatter plot with a larger data sample.

Type: Lesson Plan

Students construct and calibrate a simple hydrometer using different salt solutions. They then graph their data and determine the density and salinity of an unknown solution using their hydrometer and graphical analysis.

Type: Lesson Plan

Students will construct a scatter plot from given data. They will identify the correlation, sketch an approximate line of fit, and determine an equation for the line of fit. They will explain the meaning of the slope and y-intercept in the context of the data and use the line of fit to interpolate and extrapolate values.

Type: Lesson Plan

Continue an exploration of kinematics to describe linear motion by focusing on position-time measurements from the motion trial in part 1. In this interactive tutorial, you'll identify position measurements from the spark tape, analyze a scatterplot of the position-time data, calculate and interpret slope on the position-time graph, and make inferences about the dune buggy’s average speed

Type: Original Student Tutorial

Learn how to use the equation of a linear trend line to interpolate and extrapolate bivariate data plotted in a scatterplot. You will see the usefulness of trend lines and how they are used in this interactive tutorial.

This is part 6 in 6-part series. Click below to open the other tutorials in the series.

• Scatterplots Part 1: Graphing
• Scatterplots Part 2: Patterns, Associations and Correlations
• Scatterplots Part 3: Trend Lines
• Scatterplots Part 4: Equation of the Trend Line
• Scatterplots Part 5: Interpreting the Equation of the Trend Line

Type: Original Student Tutorial

Learn how to write the equation of a linear trend line when fitted to bivariate data in a scatterplot in this interactive tutorial.

This is part 4 in 6-part series. Click below to open the other tutorials in the series.

• Scatterplots Part 1: Graphing
• Scatterplots Part 2: Patterns, Associations and Correlations
• Scatterplots Part 3: Trend Lines
• Scatterplots Part 5: Interpreting the Equation of the Trend Line
• Scatterplots Part 6: Using Linear Models

Type: Original Student Tutorial

Explore informally fitting a trend line to data graphed in a scatter plot in this interactive online tutorial.

This is part 3 in 6-part series. Click below to open the other tutorials in the series.

• Scatterplots Part 1: Graphing
• Scatterplots Part 2: Patterns, Associations and Correlations
• Scatterolots Part 4: Equation of the Trend Line
• Scatterplots Part 5: Interpreting the Equation of the Trend Line
• Scatterplots Part 6: Using Linear Models

Type: Original Student Tutorial

Explore the different types of associations that can exist between bivariate data in this interactive tutorial.

This is part 2 in 6-part series. Click below to open the other tutorials in the series.

• Scatterplots Part 1: Graphing
• Scatterplots Part 3: Trend Lines
• Scatterolots Part 4: Equation of the Trend Line
• Scatterplots Part 5: Interpreting the Equation of the Trend Line
• Scatterplots Part 6: Using Linear Models

Type: Original Student Tutorial

Learn how to graph bivariate data in a scatterplot in this interactive tutorial.

This is part 1 in 6-part series. Click below to open the other tutorials in the series.

• Scatterplots Part 2: Patterns, Associations and Correlations
• Scatterplots Part 3: Trend Lines
• Scatterolots Part 4: Equation of the Trend Line
• Scatterplots Part 5: Interpreting the Equation of the Trend Line
• Scatterplots Part 6: Using Linear Models

Type: Original Student Tutorial

<p>Wei&nbsp;Wu discusses his statistical contributions to the Birdsong project which help to quantify&nbsp;the differences in the changes of the zebra finch's song.</p>

Type: Perspectives Video: Expert

<p>Researchers Frank Johnson, Richard Bertram,&nbsp;Wei&nbsp;Wu, and Rick&nbsp;Hyson&nbsp;explore the necessity of scientific and mathematical collaboration in modern neuroscience, as it relates to their NSF research on birdsong.</p>

Type: Perspectives Video: Expert

Chip Cotton, fishery biologist, discusses his use of mathematical regression modeling and how well the data fits his models based on his deep sea shark research.

Download the CPALMS Perspectives video student note taking guide.

Type: Perspectives Video: Professional/Enthusiast

Fishery Scientist from Florida State University discusses his new research in deep sea sharks and the unusual behavior that is found when the data is graphed.

Download the CPALMS Perspectives video student note taking guide.

Type: Perspectives Video: Professional/Enthusiast

Shark researcher, Chip Cotton, discusses the use of regression lines, slope, and determining the strength of the models he uses in his research.

Download the CPALMS Perspectives video student note taking guide.

Type: Perspectives Video: Professional/Enthusiast

Students are asked to examine a scatter plot and then interpret its meaning. Students should identify the form of the relationship (linear, curved, etc.), the direction or correlation (positive or negative), any specific outliers, the strength of the relationship between the two variables, and any other relevant observations.

Type: Problem-Solving Task

In this activity, the density of ethanol is found by graphical means. In the second part, the density of sodium thiosulfate is found, also by graphical means. The values found are then analyzed statistically.

Type: Teaching Idea

Create bar, line, pie, area, and xy graphs.

Type: Virtual Manipulative