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.
My manager thinks I know how to run a regression analysis using R. So, to save my butt, I decided to dedicate my whole weekend to learning how to do it. Think of this post as a crash course intro to learn how to brute force your way into doing one.
Skip to the section you want to read. Table of contents below:
First, to establish grounds, let me tell you what I do know about regression, and what I can do in R.
select, pipe operator
summarize, dot placeholder,
facet_grid, time series plots, axis transformations,
boxplot, slope charts)
How I figured out what to focus on this weekend.
Google search results for “r-bloggers regression.”
These are the top four links that came up for me:
A quote from the blog I chose to reference.
I clicked the link “next blog,” and BINGO! “Predict Bounce Rate based on Page Load Time in Google Analytics.” Since I didn’t mention already, to note here: I am in the performance advertising space, so this is literally right up my alley. They even do a part 3 on improving the model!
I’ve found what I’m going to focus on this weekend. Going to compile learnings here as I learn anything!
I had a really helpful conversation with an engineer who entertained my questions this weekend, and I’d like to share with you some tips that he shared. In summary, running a regression analysis is just the start of your investigation in assessing whether some data has a relationship with other data. With that context, here are ways you can ensure you come up with an analysis that is honest and helps you figure out your next steps.
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.
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