In today’s research, we focused on creating a linear regression model to look at the data on “% Obesity” while using information on “% Diabetes” and “% Inactivity.” Additionally, we created a linear regression model to investigate the “% Diabetes” data while taking into account the impact of the “% Obesity” and “% Inactivity” data.

 

A statistical technique for simulating the relationship between a dependent variable and one or more independent variables is linear regression. Finding the line that fits the data points the best is the goal of linear regression. Predictions regarding the dependent variable based on the values of the independent variables can then be made using this line.

 

The equation for a linear regression model is as follows:

y = mx + b

where;

y is the dependent variable

x is the independent variable

m is the slope of the line

b is the y-intercept of the line

 

Our understanding of the dependent variable’s response to changes in the independent variable is determined by the slope of the line, which is expressed as a ratio. When the independent variable is equal to zero, we can determine the value of the dependent variable by looking at the line’s y-intercept.