Assumptions in Linear Regression - explained | residual analysis

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1. How a residual plot is created
2. Linear relationship (03:07)
3. Equal variance - homoscedasticity (04:44)
4. Normality (06:50)
5. Cook's distance (09:30)
6. Independence (11:48)
7. No collinearity - variance inflation factor (13:33)
  1. TileStats
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Комментарии
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thanks for taking the time to explain this clearly! great video :)

Shadowster
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amazing video. I really like how you use arrows, lines and boxes in your video, which helps form a picture in my head and helps me understand difficult and abstract concepts

DHDH_DH
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Thanks for video and really help me to break stat model

analyticswithharry
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This is so helpful, would you please clarify why the unequal variance will lead to a smaller p value?

NaZhao-rf
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Plz continue with your content, is really cool, congrats

television
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It was again another clear and explanatory video by Tileststs.
I wonder if you can kindly provide a video with hand calculations on differences between various types of Anova (Type I, Type II and Type III) and their applications? There was an explanation here(link below), but not a very clear one.
Many thanks

abbasatashdehghan