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Topics

Climate Change, Interpret Data

Grades

6th, 7th, 8th

Subjects

Science, Earth and Space Sciences, Math

Duration

60 minutes

Regional Focus

North America, United States, USA - Northeast, New Jersey

Format

Google Docs, Google Sheets, Google Slides

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This lesson plan is licensed under Creative Commons.

Creative Commons License

Is Your City Getting Warmer?: Data Analysis in Google Sheets

Created By Teachers:
Last Updated:
Feb 2, 2023
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This lesson gives students the opportunity to experience a simplified version of how mathematicians and scientists use data analysis and statistics to determine how much our planet is warming due to climate change. Students will create a data table and scatter plot and use linear regression to make predictions about the future.

 

Step 1 - Inquire: Students look at a global temperature anomaly graph and discuss how this graph shows a trend of warming temperatures.

 

Step 2 - Investigate: Students analyze real-world temperature data from a New Jersey city by creating a data table and scatter plot and using linear regression to make predictions about the future.

 

Step 3 - Inspire: Students connect what they discovered about their New Jersey city to the overall trend of rising temperatures.

Positives

  • This lesson fosters independence by letting students choose their city, find their own data, create their own data table and graph, and analyze their data using guiding questions.

  • Students get to use what they learned in the lesson to practice discussing climate change with people who might be skeptical or misinformed.

Additional Prerequisites

  • Students need access to their own computers. Alternatively, teachers could have students work in partners if devices are limited.

  • Students should be familiar with graphing in Google Sheets.

Differentiation

  • Teachers could use this lesson as a mini-project to assess students’ understanding of graphing, data analysis, and/or linear regression.

  • For lower levels, teachers can instruct all students to select the same city. You can use the city from the example graph if you want to make sure there is a positive association.

  • For higher levels, students can look at multiple cities in New Jersey and compare their scatter plots.

  • This article can be used as an extension or follow-up activity for early finishers or students who are interested in learning more.

This lesson develops students' statistical technique to analyze weather data, compute trends and variance, and fit scatter plots in regression to understand climate variability in U.S. cities. All materials embedded in the lesson are thoroughly sourced. Accordingly, this lesson is recommended for classroom use.

This resource addresses the listed standards. To fully meet standards, search for more related resources.

Primary Standards

  • Mathematics
    • Statistics & Probability (6-8)
      • CCSS.MATH.CONTENT.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.
      • CCSS.MATH.CONTENT.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 (e.g., line of best fit) by judging the closeness of the data points to the line.
      • CCSS.MATH.CONTENT.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.

Supporting Standard

  • Science
    • ESS3: Earth and Human Activity
      • MS-ESS3-5. Ask questions to clarify evidence of the factors that have caused the rise in global temperatures over the past century.
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