To be confident in our conclusions, we must meet three assumptions with linear regression: linearity, normalcy, and homoscedasticity.
For starters, linear regression requires a linear relationship between the independent and dependent variables. Because linear regression is susceptible to outlier effects, it's also important to look for them.
No, just because your observed variables don't match a normal distribution doesn't mean you have to alter them. The t-test and ANOVA in linear regression analysis do not assume normality for predictors (IV) or outcomes (ANOVA) (DV).
When we use linear regression to model the relationship between a response and a predictor, we make a few assumptions. These assumptions are basically requirements that must be met before we can draw conclusions from model estimates or use a model to make a forecast.
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1.easy to understand all topic
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Akash Sambhaji
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plz provides all notes
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plzz provide notes....
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please provide notes also in pdf
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nice ☺️👍
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please provide course notes
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Shashi Kumar
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great resource to learn data science in hindi. but in this particular video lecture there is a mistake....actually mutually exclusive event can never be independent event.
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Nikhil Fapale
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it really amazing to study....and easily understand difficult concepts...i hope you make more video on like power bi and nueral network model....its really helpful....thank you for these
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