There are many cases where you won't get useful results from regression. The two most common kinds of issues are (1) when your data contain major violations of regression assumptions and (2) when you don't have enough data (or of the right kinds).
Core assumptions behind regression include
- That there is in fact a relationship between the outcome variable and the predictor variables.
- That observations are independent.
- That the residuals are normally distributed and independent of the values of variables in the model.
- That each predictor variable is not a linear combination of any others and is not extremely correlated with any others.
- Additional assumptions depend on the nature of your dependent variable; for example whether it is measured on a continuous scale or is categorical yes/no etc. The form of regression you use (linear, logistic, etc.) must match the type of data.
Not having enough data means having very few cases at all or having large amounts of missing values for the variables you want to analyze. If you don't have enough observations, your model either will not be able to run or else the estimates could be so imprecise (with large standard errors) that they aren't useful. A generic rule some people cite is that you need 10-20 cases per variable in the model; there's nothing magic about that number and you might get by just fine with less, but it suggests you could run into trouble if you have much less that that. Missing values can be a big problem as well and in the worst case could skew your results if they are not handled properly.
of, pertaining to, or determined by regression analysis: regression curve; regression equation. dictionary.com
the prefix of regression is regress
setback or regression
The sample regression function is a statistical approximation to the population regression function.
Fiendish Regression was created on 2004-08-23.
of, pertaining to, or determined by regression analysis: regression curve; regression equation. dictionary.com
Unit regression testing Regional regression testing Full regression testing
Simple regression is used when there is one independent variable. With more independent variables, multiple regression is required.
the prefix of regression is regress
Regression can be measured by its coefficients ie regression coefficient y on x and x on y.
setback or regression
Her regression is smoking.
I don't believe the graphic calculator has a cosine regression tool, but if you go to STAT, and CALC, there is a sin regression tool. If you hit enter on that then insert your L values, it will come up with a sin regression. The sin regression should be the same as a cosine regression, except that the sin regression should have a different value of C, usually getting rid of the value of C altogether will give you the correct regression.
In a regression of a time series that states data as a function of calendar year, what requirement of regression is violated?
i know the facts. What is the reason? For your Regression?
Yes.
The sample regression function is a statistical approximation to the population regression function.