Unit4, Section3: Piecing it Together
Instructional Days: 5
Real-life phenomena are often complex. Data scientists use multiple regression models to create simple equations to help explain and predict these phenomena. Data scientists can also use polynomial transformations to add flexibility to rigid linear models.
Students will read the article titled How Long Can a Spinoff Like Better Call Saul Last? that will set the
context for students to begin thinking about more than one explanatory variable to make better
predictions. The article can be found at:
S-ID 6: Represent data on two quantitative variables on a scatter plot, and describe how the variables are related.
- 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. Emphasize linear models.
Understand that multiple regression can be a better tool for predicting that simple linear regression and know when it is appropriate to use multiple regression versus simple linear regression. Understand when linear models are not appropriate based on the shape of the scatterplot.
Applied Computational Thinking using RStudio:
• Use multiple linear regression models with other predictor variables
• Fit regression lines to data and predict outcomes.
• Create non-linear models to look for relationships.
• Fit polynomials functions to data.
Economists and marketing firms use multiple regression to predict changes in the market and adjust strategies to fit the demands of changes in the marketplace.
Students will use complex sentences to construct summary statements about their understanding of data, how it is collected, how it used and how to work with it.
Students will engage in partner and whole group discussions and presentations to express their understanding of data science concepts.
Students will read informative texts to evaluate claims based on data.
Data File or Data Collection Method
NFL data set
USMNT data set
- Movies: data(movie)
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