Embedded Files
Lessons
Lessons
Focus Standards
Focus Standards
Learning Focus
Learning Focus
Additional Resources
Additional Resources
A Develop Understanding Task
Develops an understanding of the correlation coefficient as an indicator of the strength and direction of a linear model. Distinguishes between correlation and causation.
Represent data using a scatterplot.
Understand the meaning of the correlation coefficient.
Describe the difference between correlation and causation.
After collecting data, how can we tell if there is a relationship between two variables?
A Solidify Understanding Task
Connects the correlation coefficient and lines of best fit for modeling bivariate numerical data.
Model data with a linear function.
Use a linear model to analyze data.
How can we apply what we know about linear functions to statistics?
A Solidify Understanding Task
Extends the use of linear models to interpreting the slope and -intercept of regression lines with various units.
Interpret data using linear models.
Consider questions and necessary data for further research.
How can correlation coefficients and linear regressions help us to understand the differences in men’s and women’s incomes?
A Solidify Understanding Task
Introduces residuals and residual plots to analyze whether a linear model is appropriate for a given set of data.
Understand and interpret residuals.
Should all bivariate data be modeled with a linear function?
Are there other ways to tell if a linear model is appropriate besides using a correlation coefficient?
A Practice Understanding Task
Uses definitions and examples to reinforce understanding and to address misconceptions about correlation coefficients, residuals, and linear regressions.
Clarify differences between residuals and correlation coefficients.
Use precise statistical language to discuss uses of data.
What do correlation coefficients, linear regressions, and residuals really tell us about bivariate data?
Lesson 6: Food For Thought
Lesson 6: Food For Thought
A Develop Understanding Task
Reviews and extends how to interpret histograms and box plots in context. Students compare statistical representations, such as shape, center, and spread, of two single-variable numeric data sets.
Represent data with box plots, dot plots, and histograms.
Analyze data represented in different ways.
How can we describe differences in data sets? What features should we look for in different representations?
Lesson 7: Bridging The Gap
Lesson 7: Bridging The Gap
A Solidify Understanding Task
Introduces standard deviation as a measure of spread. Students use standard deviation to interpret data.
Understand standard deviation.
How does standard deviation compare to range as a measure of spread?
Lesson 8: Making the Grade
Lesson 8: Making the Grade
A Practice Understanding Task
Practices how to describe and compare data distributions.
Compare sets of data using center, spread, and shape.
How do I use measures of center and spread together to make decisions about data?
How do I compare two or more data sets that are represented by different plots?
NC.M1.S-ID.8
We know that we can fit a linear model to a set of data using technology. In this lesson, we will learn how to fit an exponential model to a set of data.
How do we determine whether a set of data is best modeled by an linear or an exponential function?
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