what is the simple linear regression equation

Hence the rejection of the null hypothesis gets easier. In our case, it’s “937.5”, which is relatively larger considering the size of the data. The larger the value than 1, the higher is the confidence in the relationship between the input and output variable. It’s a strong measure to determine the relationship between input and response variables. However, when more than one input variable comes into the picture, the adjusted R squared value is preferred. The closer the value to 1, the better the model describes the datasets and their variance. R-squared value always lies between 0 and 1. Hence in our case, how well our model that is linear regression represents the dataset. R-squared is a very important statistical measure in understanding how close the data has fitted into the model.

The line we see in our case, this value is near to zero we can say there exists a relationship between salary package, satisfaction score and year of experience. The closer it is to zero, the easier we can to reject the null hypothesis. Model t): This acronym basically depicts the p-value. Referring to the above dataset, the problem we want to address here through linear regression is:Įstimation of the salary of an employee, based on his year of experience and satisfaction score in his company.

What is the simple linear regression equation