Linear Regression - made easy

[@gregmartin]

Get my FREE cheat sheets for Statistics and Research Methods (including transcripts of these lessons) here: www.learnmore365.com/courses/statistics-research-methods-resource-library

[@Eternal-Student]

So glad to have found you as I am sure many others are. I wish I had found you before I trudged through the dull micro-lectures provided for my online University masters degree. You make the subject relative and understandable. I am going to watch everything. I need to. Thank you!

[@gregmartin]

I'm thrilled to hear that you've found the content helpful and engaging! It's always my goal to make learning as accessible and enjoyable as possible.

[@Shawn-gm4cf]

Awesome job as always. Thank you for all you contribute to stats knowledge.

[@gregmartin]

You're welcome! Glad you enjoyed it!

[@buffaloperformanceandanaly1431]

Hi Greg! Thanks for the great videos. I am trying to figure out how to filter out outliers and then run a linear model, but am getting stuck. Any suggestions?

[@jamesleleji6984]

Thanks for the video, Greg. Please could you do a video on the assumptions of linear regression as a sequel to this video. Thanks

[@gregmartin]

Will do

[@mesiazul8435]

Hi, for multiple linear regression, can a percentage change (a reduction) in the level of say blood glucose, be the continuous dependent variable?

[@eliastries]

Did anyone finde that cheat sheet Greg mentions at the end of the video?

[@psnacademy5481]

If the y-intercept is meaningless, then the line can be anywhere along the y-axis. If so, then how it will predict y for an unseen x?

[@meseretmuche6984]

Remarkable lecture, thank you so much.
Q. Please share me/us a video lecture of multiple regression plot with confidence interval and annotation of the equation and significant level?

[@carlosferrer9399]

Hi Greg. Your videos are awesome. Do you have one explaining how to perform multiple logistic regression in R?

[@gregmartin]

Thank you for your feedback and suggestion

[@dailydoseofmedicinee]

good explanation

[@gregmartin]

Thanks

[@MathwithLukgaf]

Thanks for all you do, Greg. Please I have a quick question. Please which result should I rely on (the lm or t-test) if I get the effect of the independent variable with lm but the value of p value is not less than 0.05 when I compare them with the t-test using the same data. Thanks

[@dhinojosa94]

Hello, Im not Gret but heres my 2 cents. Linear regressions and t-student tests look for different things. T-student is used to see if the mean is different between 2 groups or after an intervention. For example you could use t student to test for differences in height between boys and girls, or between children and adults. You are not trying to establish a relationship, just a difference. And inherently, your data is composed of a continous variable and another for grouping. In essence you use when you want to know IF theres a difference. Linear regressions quantify the relation between two continous variables. In this case you could use it to see if theres a relation between age and height, but notice how both age and height are continous variables. In this case you wouldnt use a lm to know if theyre different, but to track how the change in one affects the other.
Hope it helps and open to feedback if its a wrong take.

[@MathwithLukgaf]

Thanks Dr. This is helpful!

[@FantasticBiology]

Amazing!!

[@gregmartin]

Thanks!!

[@max5916]

first comment to say thanks from the heart

[@gregmartin]

Much appreciated Max !!

[@MrSaramsh]

Enjoying your content on both the channel: R Programming 101 and Global Health with Greg Martin. Thank you.

[@gregmartin]

Thanks for the feedback. Much appreciated. Glad you like the content. Gonna start a new channel on AI and Health too soon. Watch this space.

[@MrSaramsh]

@@gregmartin sure.

[@md.masumomarjashim]

Awesome

[@gregmartin]

Thanks.

[@Alexdarkkiller2]

meddl loide

[@user-fo3ug3cr4m]

Meddl