Linear Regression Model

When exploring linear regression model, it's essential to consider various aspects and implications. How should outliers be dealt with in linear regression analysis?. 8 I've published a method for identifying outliers in nonlinear regression, and it can be also used when fitting a linear model. HJ Motulsky and RE Brown. Detecting outliers when fitting data with nonlinear regression – a new method based on robust nonlinear regression and the false discovery rate. BMC Bioinformatics 2006, 7:123

Assumptions of linear models and what to do if the residuals are not .... Equally important, for your first question, I don't think that a linear regression model assumes that your dependent and independent variables have to be normal. However, there is an assumption about the normality of the residuals. regression - Interpreting the residuals vs. fitted values plot for ....

Therefore, the second and third plots, which seem to indicate dependency between the residuals and the fitted values, suggest a different model. But why does the second plot suggest, as Faraway notes, a heteroscedastic linear model, while the third plot suggest a non-linear model? Why is ANOVA equivalent to linear regression?

ANOVA and linear regression are equivalent when the two models test against the same hypotheses and use an identical encoding. The models differ in their basic aim: ANOVA is mostly concerned to present differences between categories' means in the data while linear regression is mostly concern to estimate a sample mean response and an associated $\sigma^2$. Furthermore, somewhat aphoristically one can ... Another key aspect involves, what happens when we introduce more variables to a linear regression model?.

Ask Question Asked 5 years, 8 months ago Modified 4 years, 6 months ago Building on this, regression - Why does adding more terms into a linear model always .... Many statistics textbooks state that adding more terms into a linear model always reduces the sum of squares and in turn increases the r-squared value.

Additionally, this has led to the use of the adjusted r-squ... Building on this, regression - Why are "Linear" Models so Important? GLMs are linear in parameters, that's why “linear”. See also Distinction between linear and nonlinear model and Why is polynomial regression considered a special case of multiple linear regression?

for more explanations. Why would GLMs be “general models” any more than any other models? You also got the name wrong, it stands for the generalized linear models, because it is a ... Linear regression, conditional expectations and expected values.

In the probability model underlying linear regression, X and Y are random variables. if so, as an example, if Y = obesity and X = age, if we take the conditional expectation E (Y|X=35) meaning, whats the expected value of being obese if the individual is 35 across the sample, would we just take the average (arithmetic mean) of y for those observations where X=35?