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US Historical Climate: Excel Statistical
http://serc.carleton.edu/introgeo/mathstatmodels/examples/XLstats.html

R.M. MacKay, SERC Starting Point

In this intermediate Excel activity, students import US Historical Climate Network mean temperature data into Excel from a station of their choice. They are then guided through the activity on how to use Excel for statistical calculations, graphing, and linear trend estimates. The activity assumes some familiarity with Excel and graphing in Excel.

Activity will take about two hours depending on the familiarity with Excel.

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Learn more about Teaching Climate Literacy and Energy Awareness»

Climate Literacy
About Teaching Climate Literacy

Definition of climate and climatic regions
About Teaching Principle 4
Other materials addressing 4a
Climate is not the same thing as weather – defining difference
About Teaching Principle 4
Other materials addressing 4b
Observations are the foundation for understanding the climate system
About Teaching Principle 5
Other materials addressing 5b

Excellence in Environmental Education Guidelines

1. Questioning, Analysis and Interpretation Skills:C) Collecting information
Other materials addressing:
C) Collecting information.
2. Knowledge of Environmental Processes and Systems:2.1 The Earth as a Physical System:A) Processes that shape the Earth
Other materials addressing:
A) Processes that shape the Earth.

Notes From Our Reviewers The CLEAN collection is hand-picked and rigorously reviewed for scientific accuracy and classroom effectiveness. Read what our review team had to say about this resource below or learn more about how CLEAN reviews teaching materials
Teaching Tips | Science | Pedagogy | Technical Details

Teaching Tips

  • Educator has to be careful to not allow students to draw too broad of conclusions from one station. For example, a given station may indicate a cooling trend even though the globe as a whole is warming.
  • Students unfamiliar with Excel should complete an introductory Excel activity (see resources listed in activity) before working through this activity.
  • Students should be able to work through this activity at home or in a computer lab with no supervision.

About the Science

  • Students work with real data for their home region.
  • Data used in the activity is only available until 1994, which is acceptable given the activity is about historic data, but certainly not ideal. More recent data is available on the USHCN Website so the educator can update the activity.

About the Pedagogy

  • Activity is primarily a mathematical skill builder, using data from a scientific database.
  • Questions posed in the instructions at various points in the activity help keep students on track and help them to abstract from the presented data and see the reason for the statistical analysis.
  • Activity relies heavily on Excel knowledge and skills - could be a disadvantage for some less tech-savy students.

Technical Details/Ease of Use

  • Very extensive Excel instructions and good links for further reading/help.
  • Getting current data from the interface is fast and responsive for getting data plots.
  • All required materials are downloadable from site.

Performance Expectations

HS-ESS3-5: Analyze geoscience data and the results from global climate models to make an evidence-based forecast of the current rate of global or regional climate change and associated future impacts to Earth systems.

Disciplinary Core Ideas

HS-ESS2.D1: Current models predict that, although future regional climate changes will be complex and varied, average global temperatures will continue to rise. The outcomes predicted by global climate models strongly depend on the amounts of human-generated greenhouse gases added to the atmosphere each year and by the ways in which these gases are absorbed by the ocean and biosphere.

Science and Engineering Practices

HS-P1.3: ask questions to determine relationships, including quantitative relationships, between independent and dependent variables

HS-P2.6: Develop and/or use a model (including mathematical and computational) to generate data to support explanations, predict phenomena, analyze systems, and/or solve problems.

HS-P3.4: Select appropriate tools to collect, record, analyze, and evaluate data.

HS-P3.5: Make directional hypotheses that specify what happens to a dependent variable when an independent variable is manipulated.

HS-P4.1: Analyze data using tools, technologies, and/or models (e.g., computational, mathematical) in order to make valid and reliable scientific claims or determine an optimal design solution.

HS-P4.2: Apply concepts of statistics and probability (including determining function fits to data, slope, intercept, and correlation coefficient for linear fits) to scientific and engineering questions and problems, using digital tools when feasible.

HS-P5.2: Use mathematical, computational, and/or algorithmic representations of phenomena or design solutions to describe and/or support claims and/or explanations.

HS-P6.1: Make a quantitative and/or qualitative claim regarding the relationship between dependent and independent variables.

HS-P7.4: Construct, use, and/or present an oral and written argument or counter-arguments based on data and evidence.

Cross-Cutting Concepts

HS-C1.2: Classifications or explanations used at one scale may fail or need revision when information from smaller or larger scales is introduced; thus requiring improved investigations and experiments.

HS-C1.4: Mathematical representations are needed to identify some patterns.

HS-C2.1: Empirical evidence is required to differentiate between cause and correlation and make claims about specific causes and effects.

HS-C2.2: Cause and effect relationships can be suggested and predicted for complex natural and human designed systems by examining what is known about smaller scale mechanisms within the system.

HS-C3.1: The significance of a phenomenon is dependent on the scale, proportion, and quantity at which it occurs.

HS-C3.3: Patterns observable at one scale may not be observable or exist at other scales.

HS-C3.5: Algebraic thinking is used to examine scientific data and predict the effect of a change in one variable on another (e.g., linear growth vs. exponential growth).

HS-C4.4: Models can be used to predict the behavior of a system, but these predictions have limited precision and reliability due to the assumptions and approximations inherent in models.

HS-C7.2: Change and rates of change can be quantified and modeled over very short or very long periods of time. Some system changes are irreversible.


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