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How should outliers be dealt with in linear regression analysis?
Often times a statistical analyst is handed a set dataset and asked to fit a model using a technique such as linear regression. Very frequently the dataset is accompanied with a disclaimer similar...
Why is ANOVA equivalent to linear regression? - Cross Validated
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 $\sigma^2$. Somewhat aphoristically one can ...
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?
What happens when we introduce more variables to a linear regression model?
What happens when we introduce more variables to a linear regression model? Ask Question Asked 5 years, 10 months ago Modified 4 years, 7 months ago
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. This has led to the use of the adjusted r-squared.
Linear Regression with Only Categorical Features: Evaluating the Model ...
The original Breusch-Pagan test detects linear forms of heteroskedasticity by predicting squared residuals with all predictors.
Linear Regression For Binary Independent Variables - Interpretation
For linear regression, you would code the variables as dummy variables (1/0 for presence/absence) and interpret the predictors as "the presence of this variable increases your predicted outcome by its beta".
Minimal number of points for a linear regression
What would be a "reasonable" minimal number of observations to look for a trend over time with a linear regression? what about fitting a quadratic model? I work with composite indices of inequality in health (SII, RII), and have only 4 waves of the survey, so 4 points (1997, 2001, 2004, 2008).
model - When forcing intercept of 0 in linear regression is acceptable ...
The problem is, if you fit an ordinary linear regression, the fitted intercept is quite a way negative, which causes the fitted values to be negative. The blue line is the OLS fit; the fitted value for the smallest x-values in the data set are negative.
In linear regression, when is it appropriate to use the log of an ...
Taking logarithms allows these models to be estimated by linear regression. Good examples of this include the Cobb-Douglas production function in economics and the Mincer Equation in education.
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