## Skill Introduction

Through this skill, you will learn how the regression algorithm can be applied to fit a wide variety of relationships among data. You will also investigate if the effect of an independent variable depends on the level of another independent variable by including interaction terms in the multiple regression model. Another aspect of this skill is learning how to evaluate models, regression, or otherwise, to find the most favorable levels of the independent variables. In this skill, you’ll learn how to use the Solver Add-In to find the optimal level of inputs. You will also learn about the logit transformation that is used to convert a binary outcome to a linear relationship with the independent variables. Excel does not have a built-in logistic regression tool, so you will learn how to manually design a logistic regression model, and then optimize the parameters using the Solver Add-In tool.

## Learning Outcomes

Upon successful completion, you will be able to:

• Apply the regression algorithm to fit a variety of relationships within the data

• Use the Excel Solver Add-In tool to solve constrained maximization problems

• Design a logistic regression model, and then optimize the parameters using the Excel Solver Add-In tool

## Skill Curriculum

• 1

### Orientation

• Introduction Video

• Introduction to the Skill

• Glossary

• 2

### Content and Activities

• Polynomial Regression Models

• Categorical Variables

• Multiple Indicator Variables

• Interaction Terms

• Regression Summary

• Knowledge Check 1

• Optimization with Excel Solver

• Solver Constraints and Reports

• Logit Transformation

• Simple Logistic Regression

• Logistic Regression Accuracy

• Knowledge Check 2

• 3

### Application Exercise

• Instructions and Materials

• Exercise Part 1

• Exercise Part 2

• Debriefing

• 4

### Summary

• Conclusion Video

• Final Quiz

• Survey Instructions

• Feedback Survey

• Survey Verification

• Next Steps