Q: What is the simultaneous multiple regression?

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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.

Yes they can.

True.

Simultaneous equations are where you have multiple equations, often coupled with multiple variables. An example would be x+y=2, x-y=2. To solve for x and y, both equations would have to be used simultaneously.

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.

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Simple regression is used when there is one independent variable. With more independent variables, multiple regression is required.

Cheng Hsiao has written: 'Linear regression using both temporally aggregated and temporally disaggregated data' -- subject(s): Regression analysis, Time-series analysis 'Measurement error in a dynamic simultaneous equations model with stationary disturbances' -- subject(s): Equations, Simultaneous, Errors, Theory of, Simultaneous Equations, Theory of Errors

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.

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.

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?

Yes they can.

The multiple regression statistical method examines the relationship of one dependent variable (usually represented by 'Y') and one independent variable (represented by 'X').

Yes..

True.

Simultaneous equations are where you have multiple equations, often coupled with multiple variables. An example would be x+y=2, x-y=2. To solve for x and y, both equations would have to be used simultaneously.

Not necessarily. Qualitative data could be coded to enable such analysis.