Regression diagnostics and analysis workflow

The video provides a comprehensive overview of a workflow for regression analysis, emphasizing the importance of addressing empirically testable assumptions post-analysis. It begins with formulating a hypothesis, followed by data collection and exploration to understand relationships. An initial regression model is then estimated, involving independent and dependent variables, and its results are briefly reviewed. The focus then shifts to diagnostics, favoring plots over statistical tests for a more informative view of issues like heteroskedasticity.

In the diagnostic phase, the video demonstrates the use of various plots, starting with the normal Q-Q plot to assess the distribution of residuals and identify outliers. This is followed by the residuals versus fitted plot to detect nonlinearity and heteroskedasticity in the data. The leverage versus residual squared plot helps identify influential observations. The added-variable plot is then used to examine the relationship between the dependent variable and each independent variable, isolating their unique contributions. Based on these diagnostics, adjustments are made to the regression model, such as addressing nonlinearity or heteroskedasticity, and retesting until a satisfactory model is achieved. The video concludes with the interpretation of regression coefficients in the context of the research, using the prestige dataset with 'prestige' as the dependent variable and 'education', 'income', and 'share of women' as independent variables.

[BrinderSadler]

A very informative video that is clear and uses examples so that viewers can better follow. Thank you.

[THEPSYCHOTIC]

I have been trying to find workflow videos on regression analysis for a while now, this is the first (and only one) that I found. It helped me immensely, thank you.

[magnusjensen]

Best explabation I’ve come across on YouTube! Keep up the good work

[bezaeshetu]

Thank you for the nice and clear explanation.

[ltang]

Around 7:49 are farmers less prestigious than the model predicted or more? What does sitting below the y=x line mean?

[statistikochspss-hjalpen]

Great video.
My question is what to do when ln transformation doesn't help?
Imagine a regression with only Likert scale variables (1-5). Customer satisfaction as the dependent variable and product quality, customer service as independent variables. Most customers score 4 or 5 on the all variables. Almost all of the MLR assumptions are not met. How to approach the problem?
I read about PLS being an alternative instead of OLS, but my coefficients are almost identical with both OLS and PLS (don't know if it's because of a fairly big dataset, n=8000).

[kar]

Hi Thanks for the content! 3:09, you said you have a video of the regression coefficient, I can't find it, I would like to check it out :)

[auddssey]

i want to see the r code for residual vs leverage plot, how the occupation outliers appear :-)

[zwan]

In your AV plots around 15:00 isn't it showing that the women regressor doesn't add anything to the model?