Correlation and Regression

Essential Question:

Adaptive Perseverance: Continue analyzing data despite challenges.

Learner’s Mindset: Seek out new statistical methods and tools.

Communication: Clearly present statistical findings.

Responsibility: Ensure ethical handling of data.

Global Citizenship: Use statistical analysis to address global issues.

Critical Thinking: Apply logical reasoning to interpret data.

Collaboration: Work with peers to analyze complex data sets.

What does it mean for two variables to be correlated?

How can we use regression analysis to make predictions?

What are the limitations of using correlation and regression in statistical analysis?

Understand the concept of correlation and how to calculate it.

Apply regression analysis to real-world data sets.

Critically evaluate the reliability and limitations of correlation and regression findings.

Statistical Calculation: Ability to calculate correlation coefficients and perform regression analysis.
Critical Thinking: Analyzing and interpreting statistical results to draw meaningful conclusions.
Data Handling: Proficiency in using statistical software or tools to handle and analyze data sets.

S-ID.5: Summarize categorical data for two categories in two-way frequency tables.

S-ID.6: Represent data on two quantitative variables on a scatter plot and describe how the variables are related.

CCSS.MATH.CONTENT.HSS.ID.C.8: Compute (using technology) and interpret the correlation coefficient of a linear fit.

CCSS.MATH.CONTENT.HSS.ID.C.9: Distinguish between correlation and causation.

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

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

HSS-ID.C.9: Distinguish between correlation and causation.

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