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Analyzing and interpreting data
Mini Lesson 4 (PRAC) Tide Extremes
Types of Instruction:
- Data Practice
Grades:
- 6, 7, 8
Subjects:
- Earth Science
Topics:
- Earth's Systems, Waves
Statistical Concepts:
- Correlation
Skills:
- Correlations
Graph Types:
- Scatter plot
Standards:
Using mathematics and computational thinking
Scale, proportion, and quantity
Construct an explanation based on evidence for how geoscience processes have changed Earth's surface at varying time and spatial scales.
Display numerical data in plots on a number line, including dot plots, histograms, and box plots.
Informally assess the degree of visual overlap of two numerical data distributions with similar variabilities, measuring the difference between the centers by expressing it as a multiple of a measure of variability.
Determine the meaning of words and phrases as they are used in a text, including figurative, connotative, and technical meanings; analyze the impact of a specific word choice on meaning and tone.
Determine the meaning of words and phrases as they are used in a text, including figurative, connotative, and technical meanings; analyze the impact of specific word choices on meaning and tone, including analogies or allusions to other texts.
Write arguments to support claims with clear reasons and relevant evidence.
Write arguments to support claims with clear reasons and relevant evidence.
Determine the meaning of symbols, key terms, and other domain-specific words and phrases as they are used in a specific scientific or technical context relevant to grades 6-8 texts and topics.
Integrate quantitative or technical information expressed in words in a text with a version of that information expressed visually (e.g., in a flowchart, diagram, model, graph, or table).
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.
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.