answersLogoWhite

0


Best Answer

Random error, measurement error, mis-specification of model (overspecification or underspecification), non-normality, plus many more.

User Avatar

Wiki User

โˆ™ 2010-04-27 11:54:00
This answer is:
User Avatar
Study guides

Statistics

19 cards

What are the brain's association areas

What is a field hockey stick made of

How old is she is rebecca stevenson

When during pregnancy should one quit smoking

โžก๏ธ
See all cards
4.16
โ˜†โ˜…โ˜†โ˜…โ˜†โ˜…โ˜†โ˜…โ˜†โ˜…
44 Reviews

Add your answer:

Earn +20 pts
Q: What are the sources of error in regression model?
Write your answer...
Submit
Still have questions?
magnify glass
imp
Related questions

What does f statistic mean?

The F-statistic is a test on ratio of the sum of squares regression and the sum of squares error (divided by their degrees of freedom). If this ratio is large, then the regression dominates and the model fits well. If it is small, the regression model is poorly fitting.


What does a large F-statistic mean?

The F-statistic is a test on ratio of the sum of squares regression and the sum of squares error (divided by their degrees of freedom). If this ratio is large, then the regression dominates and the model fits well. If it is small, the regression model is poorly fitting.


Is the random error in a regression equation the predicted error?

pig benis


Why are your predictions inaccurate using a linear regression model?

There are many possible reasons. Here are some of the more common ones: The underlying relationship is not be linear. The regression has very poor predictive power (coefficient of regression close to zero). The errors are not independent, identical, normally distributed. Outliers distorting regression. Calculation error.


What are some of the advantages and disadvantages of making forecasts using regression methods?

+ Linear regression is a simple statistical process and so is easy to carry out. + Some non-linear relationships can be converted to linear relationships using simple transformations. - The error structure may not be suitable for regression (independent, identically distributed). - The regression model used may not be appropriate or an important variable may have been omitted. - The residual error may be too large.


What is the difference between the logistic regression and regular regression?

in general regression model the dependent variable is continuous and independent variable is discrete type. in genral regression model the variables are linearly related. in logistic regression model the response varaible must be categorical type. the relation ship between the response and explonatory variables is non-linear.


What is a measure of the explanatory power of the regression model?

Regression analysis describes the relationship between two or more variables. The measure of the explanatory power of the regression model is R2 (i.e. coefficient of determination).


Where ridge regression is used?

Ridge regression is used in linear regression to deal with multicollinearity. It reduces the MSE of the model in exchange for introducing some bias.


If amount of error along regression line is similar is this homoscedasticity?

yyuuyuhyhyuhyuhyu


What is the difference between classical regression analysis and spatial regression analysis?

how can regression model approach be useful in lean construction concept in the mass production of houses


What is the role of the stochastic error term in regression analysis?

Regression analysis is based on the assumption that the dependent variable is distributed according some function of the independent variables together with independent identically distributed random errors. If the error terms were not stochastic then some of the properties of the regression analysis are not valid.


The random error in a regression equation?

includes both positive and negative terms.

People also asked