Simple linear regression - JMP

How to Conduct Simple Linear Regression

Understanding Regression

• Regression is a statistical method for analyzing data to determine the relationship between a response variable and various factors.

• It helps answer questions about the relationship between input and output variables, as well as identifying which input variable has the most influence on the response.

Applications of Regression

• Regression is used in predictive modeling to forecast responses based on specific input variables.

• It also plays a role in optimization, where optimal values of input variables are determined for desired response outcomes.

Types of Linear Regression

• Simple linear regression involves one factor, while multiple linear regression includes multiple factors.

Conducting Simple Linear Regression Using JMP

Using Graph Builder

• Start by using the graph builder menu; drag and drop your response variable (Y) and variable of interest (X).

• JMP automatically generates a scatter plot with a smoother line; you can adjust this to explore complex relationships.

• To fit a simple line, select the line fitting option. JMP will display a fit line along with confidence intervals.

Analyzing Results

• The equation generated indicates how changes in the independent variable affect the dependent variable (e.g., an increase of 1 unit in Variable 1 results in an increase of 0.01652 units in Y).

Using Fit Y by X for Simple Linear Regression

Steps for Fit Y by X

• Open the Fit Y by X platform; choose your response (Y variable) and factor (X).

• JMP will show a bivariate analysis scatter plot; select rectangle option and then fit line to perform simple linear regression.