options: Ratios & Proportional Relationships: students learn to recognize, describe, represent, and reason with ratios, rates, proportional relationships, and percent.

Functions: students learn to define, use, and evaluate functions to model relationships between quantities.

Counting & Cardinality: students learn to know number names and the count sequence, count to tell the number of objects, and compare numbers.

Modeling: students learn to choose and use appropriate mathematics and statistics to analyze real-world empirical situations, improve decisions, and report on their conclusions and the reasoning behind them.

Number Systems and Quantity: students learn to understand the complex number system and reason quantitatively.

Geometry: students learn to classify geometric figures by their properties, and understand and compare the relationships between them. Students develop and use formulas to compute lengths, areas, and volumes, and they use transformations to generate new shapes and compare existing ones. Geometry can be studied with and without coordinates.

Operations & Algebra: students learn to solve problems using algebraic thinking and operations such as addition, subtraction, multiplication, and division. They may learn to identify and explain arithmetic patterns, evaluate or manipulate numerical expressions, and reason with equations and inequalities.

Number & Operations - Fractions: students learn to understand fractions as numbers and may work with them using addition, subtraction, multiplication, and division.

Number & Operations in Base Ten: students learn to work with the base-ten system and build place value understanding.

Measurement & Data: students learn to work with data and measure attributes such as time, money, length, area, and volume. They may learn to compare measurements to operations and convert between different units of measure.

Statistics & Probability: students learn to analyze and produce data distributions, and build understanding of univariate and bivariate data. They learn to interpret data, make statistical inferences, justify conclusions, understand rules of probability, and use probability to make decisions.
problem_activity: Optional activity
In the previous activity, students drew a line that best fit the latitude-temperature data and found the equation of this line. The line is a mathematical model of the situation. In this lesson, they use their model to make predictions about temperatures in cities that were not included in the original data set. In the next activity, they also interpret the slope of the line and the intercepts in the context of this situation. This leads to a discussion of the limitations of the mathematical model they developed.
Launch
Students in same groups of 3–4. If available, tell the students the latitude and average high temperature in September in their city.
Action and Expression: Internalize Executive Functions.
Chunk this task into more manageable parts to support students who benefit from support with organizational skills in problem solving. For example, present one question at a time and monitor students to ensure they are making progress throughout the activity.
Supports accessibility for: Organization; Attention
Student Facing
In the previous activity, you found the equation of a line to represent the association between latitude and temperature. This is a
mathematical model
.
Use your model to predict the average high temperature in September at the following cities that were not included in the original data set:
Detroit (Lat: 42.14)
Albuquerque (Lat: 35.2)
Nome (Lat: 64.5)
Your own city (if available)
Draw points that represent the predicted temperatures for each city on the scatter plot.
The actual average high temperature in September in these cities were:
Detroit:
\(74^\circ\text{F}\)
Albuquerque:
\(82^\circ\text{F}\)
Nome:
\(49^\circ\text{F}\)
Your own city (if available):
How well does your model predict the temperature? Compare the predicted and actual temperatures.
If you added the actual temperatures for these four cities to the scatter plot, would you move your line?
Are there any outliers in the data? What might be the explanation?
Student Response
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Activity Synthesis
Invite some groups to share their results and compare predictions for the different cities. Ask if students think that the line is a good mathematical model to predict the temperature in a location if you know the latitude.
Representing, Speaking, Listening: MLR2 Collect and Display.
Use this routine to collect verbal and written ideas about students’ mathematical models and the limitations of them that they see. During the synthesis discussion, chart key ideas that students share out about the slope, intercept, and differences in their predictions. Remind students that we call this a “mathematical model” (i.e., the line they drew). It is one way to look at data. Ask students, “What are the limitations of this model in making predictions?” Add their language to your chart. Reference this chart in the following lesson and make revisions as needed with student input.
Design Principle(s): Support sense-making; Maximize meta-awareness


correct_option_index: 1

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