![]() ![]() ![]() 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. ![]() “salary_in_lakhs” is the output variable. Now we have a dataset where “satisfaction_score” and “year_of_Exp” are the independent variable. ![]()
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December 2022
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