Greetings, fellow data enthusiasts! I’m thrilled to share my journey into the interesting area of linear regression with you today.
The simplicity of linear regression is what initially drew me to it. We all have an innate understanding of the process, which is essentially about finding the best-fit line between data points. Finding hidden connections between variables is similar to putting puzzle pieces together.
The linear regression equation is as follows
Y=+α+β⋅X+ε
Here Y is the dependent variable that we are going to predict, and X is our independent variable
If we apply the above equation to the Data which The dependent variable is the %diabetes=Y, while the independent variable is the %inactivity.=X
then we get “%diabetes = α + β %inactivity + ε”, using this equation we can predict the %diabetic using %inactivity
And the coming to Multilinear regression helps to enhance the simplicity of the preceding statement by allowing for several independent variables. In other words, we can take into account the combined impact of multiple factors on our dependent variable rather than being restricted to just one predictor
This can be used when there are multiple variables in our data set We can observe that there are 3 variables % obesity, % inactivity, and %diabetes
Here we are going to find the %diabetes (dependent variable ) and two independent variables which are % obesity, % inactivity
We can also observe that there are 1370 points of %inactivity and for every %inactivity there is a %diabetes so we have common point of data sets between them.
In upcoming days we are going to find the relation between all three %diabetes % obesity, % inactivity