## Course overview

Linear regression shows us how we can use data to predict the value of an outcome. This course covers the structure of a linear regression model, how to interpret it, how to determine if a model is appropriate, and how to use the model to predict values of new data.

In this course, you’ll learn to create single and multiple linear regressions, identify the different types of predictors, and identify a cost function for linear regression. You’ll also learn how to interpret regression parameters, how to check linear regression fit, and how to apply linear regression models.

You will use tools such as scikit-learn, statsmodels, pandas, NumPy and matplotlib.

Best of all, you’ll learn by doing — you’ll practice and get feedback directly in the browser. At the end of the course, you’ll combine your new skills to complete a project to predict insurance costs.

## Key skills

- Describing a linear regression model
- Constructing a linear regression model and evaluating it based on the data
- Interpreting the results of a linear regression model
- Using a linear regression model for inference and prediction

## Course outline

### Linear Regression Modeling in Python [5 lessons]

### Introduction to Linear Regression 1h

Lesson Objectives- Define the advantages of a linear regression model
- Identify the intercept, coefficients, and error of a linear regression model
- Create a simple linear regression
- Create a multiple linear regression
- Identify different types of predictors
- Identify a cost function for linear regression

### Interpreting Regression Parameters 1h

Lesson Objectives- Create and fit a LinearRegression object on data
- Interpret the intercept of a linear regression
- Interpret the slopes of a linear regression
- Identify changes to interpretations when categorical variables are used

### Checking Linear Regression Fit 1h

Lesson Objectives- Describe the assumptions of linear regression
- Assess the homoskedasticity of a model via a plot
- Assess trends in the model error
- Calculate and interpret the R-squared of a model

### Applying Linear Regression Models 1h

Lesson Objectives- Distinguish between a prediction problem and an inference problem
- Calculate the test error from a model
- Select features that will be useful in a predictive regression model Communicate the results of a linear regression model

### Guided Project: Predicting Insurance Costs 1h

Lesson Objectives- Investigate data and choose predictors
- Create a linear model based
- Evaluate the model diagnostics
- Interpret the model results

## Projects in this course

### Predicting Insurance Costs

For this project, you’ll step into the role of a data analyst tasked with developing a model to predict patient medical insurance costs based on demographic and health data.

## The Dataquest guarantee

Dataquest has helped thousands of people start new careers in data. If you put in the work and follow our path, you’ll master data skills and grow your career.

We believe so strongly in our paths that we offer a full satisfaction guarantee. If you complete a career path on Dataquest and aren’t satisfied with your outcome, we’ll give you a refund.

## Master skills faster with Dataquest

### Go from zero to job-ready

Learn exactly what you need to achieve your goal. Don’t waste time on unrelated lessons.

### Build your project portfolio

Build confidence with our in-depth projects, and show off your data skills.

### Challenge yourself with exercises

Work with real data from day one with interactive lessons and hands-on exercises.

### Showcase your path certification

Share the evidence of your hard work with your network and potential employers.