# Unit4, Section3: Piecing it Together

Instructional Days: 5

## Enduring Understandings

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.

## Engagement

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:
http://fivethirtyeight.com/features/how-long-can-a-spinoff-like-better-call-saul-last/

## Learning Objectives

Statistical/Mathematical:

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.

Data Science:

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.

• Fit polynomials functions to data.

Real-World Connections:

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.

## Language Objectives

1. Students will read informative texts to evaluate claims based on data.

2. Students will engage in partner and whole group discussions about how adding variables to a model will help or hinder its predictions.

3. Students will construct their own linear model using multiple variables to compare and contrast which model makes the best predictions.

## Data File or Data Collection Method

Data File:

1. Movies: `data(movie)`

2. Cereal brands: `data(cereal)`

Data Collection:

Students will collect data for their Team Participatory Sensing campaign.