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Assumptions of linear models and what to do if the residuals are not ...
Check different kind of models. Another model might be better to explain your data (for example, non-linear regression, etc). You would still have to check that the assumptions of this "new model" are not violated. Your data may not contain enough covariates (dependent variables) to explain the response (outcome).
regression - What intuitively is "bias"? - Cross Validated
In regression we can get biased estimators of slopes by doing stepwise regression. A variable is more likely to be kept in a stepwise regression if the estimated slope is further from 0 and more likely to be dropped if it is closer to 0, so this is biased sampling and the slopes in the final model will tend to be further from 0 than the true slope.
What happens when we introduce more variables to a linear regression model?
$\begingroup$ Confounding can be reduced when more confounders are added to the model, which increases the likelihood of identifying true associations among exposures of interest. Causality is difficult to establish and it is safe to say that no single cohort study can establish causality.
regression - How to interpret glm and ols with offset - Cross Validated
The interpretation of the other variables (linear parameters in the linear predictor $\eta$ should then be clear, it is interpreted as usual in regression models. Please note that the exact choice of distribution family in the explanation above do not play any role!
Does it make sense to use a date variable in a regression?
Perhaps the simplest case is linear regression on a date variable in years. Here a regression of some response on date expressed as dates like 2000 or 2010 implies an intercept which is the value of response in year 0. Setting aside the calendrical detail that there was no such year, such an intercept is often absurdly large positive or ...
Regression with skewed data - Cross Validated
While sometimes linear regression is a good approximation for limited dependent variables (for example, in the case of binary logit/probit), oftentimes it is not. Enter Generalized Linear Models. In your case, because the outcome variable is count data, you have several choices: Poisson model; Negative Binomial model; Zero Inflated Poisson (ZIP ...
Simple explanation of dynamic linear models - Cross Validated
The most common question is "what does the state variable represent?" The answer to that depends on the model, but most DLMs can be thought of as a regression with a time-varying coefficient. In this context, those time-varying coefficients are your states usually. If you regress on an intercept, they sometimes call that model a local level model.
Assessing the Contribution of each Predictor in Linear Regression
It means basically nothing to compare the values of the regression coefficients unless the predictors are standardized and the model is specified correctly, especially when the predictors are inter-correlated (which is definitely the case - look at the warning at the bottom of the output). Just see what happens to the coefficients if you drop ...
regression - Explain model adjustment, in plain English - Cross Validated
An alternative way of adjusting/controlling for variables that is particularly useful when there are many of them is provided by regression analysis with multiple dependent variables, sometimes known as multivariable regression analysis. (There are different types of regression models depending on the type of outcome variable: least squares ...
Getting negative predicted values after linear regression
$\begingroup$ marcL -- There are three main problems with the model you fitted: (1) the relationship isn't linear; (2) the model you chose doesn't respect a known bound; (3) the spread isn't constant. The fact that the transformation would also make the conditional distribution less skew would be a bonus, rather than a requirement.
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