Statistical Tableau

Chapter 8. Linear Regression in Tableau

In the previous chapters, you learned different methods you can use to understand your data better. Confidence intervals allow you to make inferences about the data, anomaly detection can help you find outliers, and knowing the distribution of your data is a foundational skill every analyst should have. Starting in this chapter, you will learn how to incorporate predictive models within your visualizations, starting with simple linear regression. This is a great model that allows you to make inferences about your data and predict future outcomes, and it is very easy to explain to your stakeholders.

By the end of this chapter, you will have a basic understanding of simple linear regression, know how to implement it in Tableau, and be able to interpret the results of the statistical summary.

Linear Regression Model

Linear regression has many applications and is used across many different industries. A few examples of these applications are:

Predicting housing prices

In real estate, linear regression can be used to predict housing prices based on various factors such as the size of the house, number of bedrooms, location, and other relevant features. By fitting a linear regression model to historical data of house sales, one can estimate the price of a new property on the market.

Sales forecasting

Linear regression can be employed in sales and marketing to forecast product sales based on factors such as advertising spending, seasonality, ...