Predicting Future Values
Learning Outcomes
Get More Info
data, Tech & ai skills in
What’s Included
Introducing Prediction with Regression
Introducing Prediction with Regression
Regression analysis is an AI technique that studies patterns in past data to produce a formula that lets us predict future values. We'll introduce this concept in this lesson.
Values Used in Regression
Values Used in Regression
Regression relies on two key values: the output that we want to predict, and the input or inputs used to predict it. This lesson introduces the important properties of these inputs and outputs.
Datasets Used in Prediction
Datasets Used in Prediction
The aim of regression is to use past data to understand the future, so it's important we have two datasets: one to formulate our model, and one to test how accurate its predictions are. We'll learn about these two datasets in this lesson.
Determining the Relationships
Determining the Relationships
The key concept of a regression model is to understand the exact relationship between the input and output variables. This lesson will explain how we can identify this relationship.
Analyzing a Regression Equation
Analyzing a Regression Equation
The output of a regression model is an equation that explains the relationship between inputs and outputs, letting us predict the output with data on the input variables. We'll learn how to make these predictions in this lesson.
Understanding the Impact of Predictors
Understanding the Impact of Predictors
Not every input variable in a regression model has a meaningful impact on the output. In this lesson, we learn how to identify which inputs are meaningful and which are not by analyzing the regression model.
Evaluating Predictive Strength
Evaluating Predictive Strength
Not all regression models accurately explain the relationship between input and output variables. In this lesson, we'll learn how to measure the predictive strength of a regression model.
Data Requirements for Prediction
Data Requirements for Prediction
There are certain data requirements that must be met for a linear regression model to be appropriate. This lesson explains the relevant requirements for the input and output variables.
Additional Data Requirements
Additional Data Requirements
Following on from the previous lesson, this model explains the assumptions of linear regression that relate to the model's residuals, that is the gaps between the model's predicted values and actual values for the output variable.
Pitfalls of Regression
Pitfalls of Regression
A regression model doesn't always perfectly explain the relationship between input and output variables. This lesson explains why predictions made using a regression model may not always be accurate.
Certifications
A Learner Experience Designed to Deliver Transformation
How Leading Teams Use Kubicle to Upskill & Excel
