?? Demystifying OLS in Linear Regression! ??
Shirshak Mohanty, MBA, MPH
Masters of Public Health at NYU | Global Healthcare Specialist | Data Analyst | AI Integration Enthusiast
In the dynamic world of data, understanding concepts like Ordinary Least Squares (OLS) in linear regression is crucial. Let's make it simple!
?? What is OLS? Ordinary Least Squares is a technique to estimate the parameters in a linear relationship between variables. It helps us find the best-fitting line through data points by minimizing the sum of the squared differences between observed values and those predicted by our model.
?? Why Does it Matter? OLS is a cornerstone in regression analysis, providing a clear path to understand relationships between variables. From predicting market trends ?? to enhancing healthcare outcomes ??, OLS is a key player.
? Key Takeaway:
Linear Relationship: Assumes a straight-line relationship between dependent and independent variables.
Minimizing Residuals: Focuses on reducing the gaps between actual and predicted values.
Assumptions Matter: Relies on principles like linearity, homoscedasticity, and normally distributed residuals.
Embracing OLS empowers us to make data work for us. So, whether you're in finance, whether you're in finance, healthcare, or any field in between, understanding OLS is a crucial step towards data-driven success! ??
#DataScience #LinearRegression #AI #Analytics #Healthcare #MachineLearning
Project Management Professional (PMP)? | Health Informatics | Digital Health Solutions | Health Technology | Data Mining | Analytics | Electronic Health Record | Survey Systems | Software Development | AI | IT Consultant
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