Using regression with correlated data. Tutorial (including R code) for using Generalized Estimating Equations and Multilevel Models.

While regression models are easy to run given their short, simple syntax, this accessibility also makes it easy to use regression inappropriately. These models have several key assumptions that need to be met in order for their output to be valid, but your code will typically run whether or not these assumptions have been met.

Video tutorial

For linear regression (used with a continuous outcome), these assumptions are as follows:

**Independence: All observations are independent of each other, residuals are uncorrelated**- Linearity: The relationship between X and Y is linear
- Homoscedasticity: Constant variance of residuals at different values of X
- Normality: Data should be normally distributed around the regression line

A data scientist/analyst in the making needs to format and clean data before being able to perform any kind of exploratory data analysis.

Learn the essential concepts in data science and understand the important packages in R for data science. You will look at some of the widely used data science algorithms such as Linear regression, logistic regression, decision trees, random forest, including time-series analysis. Finally, you will get an idea about the Salary structure, Skills, Jobs, and resume of a data scientist.

Best Data Science With R Training in Hyderabad - We Provides Best Data Science Certification Courses in Hyderabad offering extensive Data Science With R Training by Data scientists. Enrol Today!

Online Data Science Training in Noida at CETPA, best institute in India for Data Science Online Course and Certification. Call now at 9911417779 to avail 50% discount.

7 steps to run a linear regression analysis using R. I learned how to do regression analysis in R using brute force. With these 7 copy and paste steps, you can too.