Montana Math MT.CORE.DATA.INT.6.c
The Standard
- MT.CORE.DATA.INT.6
- Analyze the relationship between two quantitative data distributions in context that have a linear association. This standard should incorporate cultural context relating to Montana Indigenous Peoples and local communities by: Using technology strategically, represent two quantitative data distributions on scatter plots, Describing verbally how the variables are related, Using technology to find the least-squares regression line (line of best) fit for two quantitative variables, Understanding that the line of best fit minimizes the square of the residuals, and Understanding correlation as a measure of linear association and using technology, compute the correlation coefficient of a linear relationship.
Analyze the relationship between two quantitative data distributions in context that have a linear association. This standard should incorporate cultural context relating to Montana Indigenous Peoples and local communities by: Using technology to find the least-squares regression line (line of best) fit for two quantitative variables,
2026 Montana Mathematics Content Standards · Data Reasoning and Probability
Teacher's field guide
What This Standard Means
What Students Need to Do
- Students plot paired measurements and check whether the points follow a roughly straight pattern. They use Desmos or a spreadsheet to fit a least-squares line, then explain what the line says about the measurements.
What Mastery Looks Like
- A student can fit a line to Browning's monthly average high and low temperatures and explain the slope with units. They can estimate a low temperature from a high within the data range and explain why a monthly average cannot forecast one night.
Common Misconceptions
- Students may swap the axes and describe the slope backward. They may think the line must pass through every point, or treat a prediction from monthly averages as a forecast for a particular day.
How to Assess It
- Give these practice pairs of high and low temperatures in °F, not observed weather: (35, 14), (45, 23), (55, 30), (65, 39), (75, 46). Ask students to use Desmos to fit a least-squares line, predict the low when the high is 60°F, and explain the slope.
Lesson moves
Ways to Teach It
Give students a printed table of Browning's monthly average highs and lows; have them plot the pairs on graph paper and fit a line in Desmos.
Ask students to write what the slope says about Browning's monthly highs and lows, and why the line misses some points.
Give pairs of students different high temperatures; have them use the fitted line to predict lows, then check which predictions stay within the data range.
For North American Indian Days in Browning, ask what a summer temperature estimate could help planners consider and why it cannot forecast one night's weather.
Keep exploring
Related Standards
- MT.CORE.DATA.INT.6.a
Using technology strategically, represent two quantitative data distributions on scatter plots,
- MT.CORE.DATA.INT.6
Analyze the relationship between two quantitative data distributions in context that have a linear association. This standard should incorporate cultural contex...
- MT.CORE.DATA.INT.6.d
Understanding that the line of best fit minimizes the square of the residuals, and
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