Skip to main content

Stage 1 Desired Results

UNIT 1: Two Variable Data

Pacing: 3 Weeks

Stage 1 Desired Results

Established Goals:

  • ID.B.5: Summarize categorical data for two categories in two-way frequency tables.  Interpret relative frequencies in the context of the data (including joint, marginal, and conditional relative frequencies).  Recognize possible associations and trends in the data. 

  • ID.B.6: Represent data on two quantitative variables on a scatter plot, and describe how the variables are related. 

    • ID.B.6.A: Fit a function to the data; use functions fitted to data to solve problems in the context of the data.  Use given functions or choose a function suggested by the context. 

    • ID.B.6.B: Informally assess the fit of a function by plotting and analyzing residuals.

    • ID.B.6.C: Fit a linear function for a scatter plot that suggests a linear association. 

  • ID.C.7: Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data.

  • ID.C.8: Compute (using technology) and interpret the correlation coefficient of a linear fit. 

  • ID.C.9: Distinguish between correlation and causation.

Transfer

Students will be able to independently use their learning to….

  • Apply the principles learned to analyze and interpret data in various practice contexts. 

  • Recognize and apply analysis concepts when dealing with more than two variables.

  • Apply analysis skills to solve problems.

Meaning

Understandings

Students will understand…

  • The fundamental concept of analyzing relationships between two variables.

  • The ability to interpret scatterplots and identify patterns or trends in data.

  • The concept of correlation, distinguishing between correlation and causation.

Essential Questions

Students will keep considering…

  • What are the different types of relationships between two variables, and how are they characterized?

  • What is the difference between correlation and causation, and why is it important to recognize this distinction?

  • How can regression analysis be used to model and predict the relationship between two variables?

Acquisition

Students will know…

  • How to calculate and interpret correlation coefficients and regression parameters. 

  • The assumptions and mechanics of linear regression analysis. 

  • The impact of outliers.

Students will be skilled at…

  • Using technology to analyze two-variable data. 

  • Communicating findings from analysis effectively.

Back to top