Sign in or start a free trial to avail of this feature.
1. Understanding Linear Regression
In this lesson, you'll learn the basics of linear regression and how it can be applied.
Regression in Alteryx (00:12)
Alteryx can help us conduct linear and logistic regression. To do this, you need to download the Alteryx predictive analytics add-in. This can be downloaded from the Alteryx admin portal. If the add-in is downloaded, you will see a predictive tab on the tools palette.
Lesson Goal (00:41)
The goal of this lesson is to understand what linear regression is and why it can be useful.
Course Case Study (00:48)
A construction company uses different blends of concrete to achieve different properties. The company experiments with different material combinations, costing time and money. They want to understand more precisely what outcomes will result from particular material combinations.
Linear Regression Functions (01:20)
A function takes in input values, transforms those values according to some specific parameters, and outputs the transformed value. For our construction company, the input values are a blend of different concrete ingredients, and the output is the compressive strength of the blend. The challenge is to identify the transformation that occurs within the function.
Predictive models like linear regression analyze inputs and outputs and aim to identify the function that transforms the specified inputs into the specified outputs. Some complex models, like neural networks, are referred to as black boxes, as we cannot see how they transform their inputs. By contrast, models such as linear regression are completely transparent, allowing us to apply the linear regression function to data beyond the data used to create it.
Linear regression examines a series of inputs and outputs, and produces a formula that aims to map the inputs to the outputs. We can then use this formula to predict outputs for future inputs.
Linear Regression Plots (03:20)
A scatter plot of inputs and outputs can be used to visualize the concept of linear regression. A perfect model would constantly bend and curve to hit every point. Linear regression models the relationship between the points using a single line. This line exactly hits few, if any, of the points, but it should be close enough for most points. This example illustrates that linear regression requires data to be in a roughly linear shape in order to be effective. This happens when our data is correlated.