## 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 2h

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 2h

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 2h

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 2h

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 2h

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

## Projects in this course

### Guided Project: Predicting Insurance Costs

In this guided project, practice linear regression modeling and evaluation.

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