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There is multicollinearity in regression when the variables are highly correlated to each other. For example, if you have seven variables and three of them have high correlation, then you can just use one them in your dependent variable rather than using all three of them at the same time. Including multicollinear variables will give you a misleading result since it will inflate your mean square error making your F-value significant, even though it may not be significant.
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What is the difference between multiple regression and logistic regression?
In cases wherethe dependent variable can take any numerical value for a given set of independent variables multiple regression is used.But in cases when the dependent variable is qualitative(dichotomous,polytomous)then logistic regression is used.In Multiple regression the dependent variable is assumed to follow normal distribution but in case of logistic regression the dependent variablefollows bernoulli distribution(if dichotomous) which means it will be only0 or 1.
Can dummy variables be used in multiple linear regression analysis?
Yes they can.
Multiple regression analysis examines the relationship of several dependent variables on the independent variable?
True.
How are beta coefficients interpreted differently for regression and multiple regression?
Beta is just the slope (B0 is the y-intercept), and you have Bn coefficients where n is the number of regressors. In other words, it is the amount of change in y you would expect with a given change in x. When you deal with multiple regression, you will have a matrix (just one column though, so a vector) of beta values corresponding to your regressors.
What is the adjective of the word regression?
of, pertaining to, or determined by regression analysis: regression curve; regression equation. dictionary.com
Simple regression and multiple regression?
Simple regression is used when there is one independent variable. With more independent variables, multiple regression is required.
What is the difference between multiple regression and logistic regression?
In cases wherethe dependent variable can take any numerical value for a given set of independent variables multiple regression is used.But in cases when the dependent variable is qualitative(dichotomous,polytomous)then logistic regression is used.In Multiple regression the dependent variable is assumed to follow normal distribution but in case of logistic regression the dependent variablefollows bernoulli distribution(if dichotomous) which means it will be only0 or 1.
What is the difference betwene simple linear regression and multiple regression?
Simple linear regression is performed between one independent variable and one dependent variable. Multiple regression is performed between more than one independent variable and one dependent variable. Multiple regression returns results for the combined influence of all IVs on the DV as well as the individual influence of each IV while controlling for the other IVs. It is therefore a far more accurate test than running separate simple regressions for each IV. Multiple regression should not be confused with multivariate regression, which is a much more complex procedure involving more than one DV.
What is the difference between simple and multiple linear regression?
I want to develop a regression model for predicting YardsAllowed as a function of Takeaways, and I need to explain the statistical signifance of the model.
An author is most likely to defend her choice of multiple regression statistical techniques in which section of a proposal?
An author is most likely to defend her choice of multiple regression statistical techniques in which section of a proposal?
Can dummy variables be used in multiple linear regression analysis?
Yes they can.
The multiple regression statistical method is used to examine what relationship?
The multiple regression statistical method examines the relationship of one dependent variable (usually represented by 'Y') and one independent variable (represented by 'X').
Multiple regression analysis examines the relationship of several dependent variables on the independent variable?
True.
Is multiple regression a quantitative data anaysis?
Not necessarily. Qualitative data could be coded to enable such analysis.
How are beta coefficients interpreted differently for regression and multiple regression?
Beta is just the slope (B0 is the y-intercept), and you have Bn coefficients where n is the number of regressors. In other words, it is the amount of change in y you would expect with a given change in x. When you deal with multiple regression, you will have a matrix (just one column though, so a vector) of beta values corresponding to your regressors.
What is the difference between multivariate regression and multiple regression?
Although not everyone follows this naming convention, multiple regression typically refers to regression models with a single dependent variable and two or more predictor variables. In multivariate regression, by contrast, there are multiple dependent variables, and any number of predictors. Using this naming convention, some people further distinguish "multivariate multiple regression," a term which makes explicit that there are two or more dependent variables as well as two or more independent variables.In short, multiple regression is by far the more familiar form, although logically and computationally the two forms are extremely similar.Multivariate regression is most useful for more special problems such as compound tests of coefficients. For example, you might want to know if SAT scores have the same predictive power for a student's grades in the second semester of college as they do in the first. One option would be to run two separate simple regressions and eyeball the results to see if the coefficients look similar. But if you want a formal probability test of whether the relationship differs, you could run it instead as a multivariate regression analysis. The coefficient estimates will be the same, but you will be able to directly test for their equality or other properties of interest.In practical terms, the way you produce a multivariate analysis using statistical software is always at least a little different from multiple regression. In some packages you can use the same commands for both but with different options; but in a number of packages you use completely different commands to obtain a multivariate analysis.A final note is that the term "multivariate regression" is sometimes confused with nonlinear regression; in other words, the regression flavors besides Ordinary Least Squares (OLS) linear regression. Those forms are more accurately called nonlinear or generalized linear models because there is nothing distinctively "multivariate" about them in the sense described above. Some of them have commonly used multivariate forms, too, but these are often called "multinomial" regressions in the case of models for categorical dependent variables.
What is the adjective of the word regression?
of, pertaining to, or determined by regression analysis: regression curve; regression equation. dictionary.com
