Contents
What is an overestimate and underestimate?
When the estimate is higher than the actual value, it’s called an overestimate. When the estimate is lower than the actual value, it’s called an underestimate.
Is normality and assumption of linear regression?
denotes a mean zero error, or residual term. To carry out statistical inference, additional assumptions such as normality are typically made. As a consequence, for moderate to large sample sizes, non-normality of residuals should not adversely affect the usual inferential procedures. …
Is it better to overestimate or underestimate?
But the penalty for underestimation is nonlinear and unbounded—planning errors, shortchanging upstream activities, and the creation of more defects cause more damage than overestimation does, and with little ability to predict the extent of the damage ahead of time. Don’t intentionally underestimate.
When to use an overspecified regression model?
Regression models that are overspecified yield unbiased regression coefficients, unbiased predictions of the response, and an unbiased MSE. Such a regression model can be used, with caution, for prediction of the response, but should not be used to ascribe the effect of a predictor on the response.
Are there any missing predictors in the regression model?
That is, there are no missing, redundant or extraneous predictors in the model. Of course, this is the best possible outcome and the one we hope to achieve! The good thing is that a correctly specified regression model yields unbiased regression coefficients and unbiased predictions of the response.
What happens when you add redundant predictors to a regression equation?
That is, part of the model is correct, but we have gone overboard by adding predictors that are redundant. Redundant predictors lead to problems such as inflated standard errors for the regression coefficients. (Such problems are also associated with multicollinearity, which we’ll cover in Lesson 12).
When do we use regression to make predictions?
When we use regression to make predictions, our goal is to produce predictions that are both correct on average and close to the real values. In other words, we need predictions that are both unbiased and precise.