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Q: When using simple linear regression to analyze the estimated price for a used car independent variables would most likely result in the most accurate estimating equation?
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What is regression analysis?

In statistics, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables.


Simple regression and multiple regression?

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


What are explanatory variables in regression?

Explanatory (or independent) variables are variables such that changes in their value are thought to cause changes in the "dependent" variables.


What is the line of regression?

line that measures the slope between dependent and independent variables


Multiple regression analysis examines the relationship of several dependent variables on the independent variable?

True.


What is the variation attributable to factors other than the relationship between the independent variables and the explained variable in a regression analysis is represented by?

Regression mean squares


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 mathematical procedure that predicts the dependent variable on the basis of knowledge known about independent variables?

regression analysis


What is multi collinearity?

Multicollinearity is the condition occurring when two or more of the independent variables in a regression equation are correlated.


What are the advantages of regression over correlation?

Correlation is a measure of association between two variables and the variables are not designated as dependent or independent. Simple regression is used to examine the relationship between one dependent and one independent variable. It goes beyond correlation by adding prediction capabilities.


Can independent variables be changed?

Yes. In fact, in multiple regression, that is often part of the analysis. You can add or remove independent variables to the model so as to get the best fit between what values are observed for the dependent variable and what the model predicts for the given set of independent variables.


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.