35 terms

Coefficient of Determination

R-Squared

Constant Elasticity Model

Elasticity of the DV, with respect to IV is constant; in multiple regression, both variables appear in logarithmic form

Control Variable

Explanatory variable

Covariate

Explanatory variable

Degrees of Freedom

the number of observations minus the number of estimated paramaters i.e. n-2 in a simple regression analysis

Dependent Variable

The variable to be explained in a multiple regression mode

Elasticity

The percentage change in one variable given a 1% ceteris paribus increase in another variable

Error Term (Disturbance)

The variable in a simple or multiple regression equation that contains unobserved factors that affect the DV. Error term may also include measurement errors in the observed dependent or independent variables

Error Variance

The variance of the error term in a multiple regression model

Explained Sum of Squares (SSE)

Total sample variation of the fitted value in a multiple regression model

Explained Variable

The variable that is used to explain variation in the dependent variable

F statistic

used to test multiple hypotheses about the parameters in a multiple regression model

First Order Conditions

Set of Linear equations used to solve for the OLS estimates

Fitted Value

the estimated values of the dependent variable when the values of the independent variable s for each observation are plugged into the OLS regression line

Gauss Markov assumptions

MLR1-MLR5

Heteroskedasticity

the variance of the error term, given the explanatory variables is not constant

Homoskedasticity

the errors in a regression model have constant variance conditional on the explanatory variables

Intercept Parameter

The parameter in a multiple linear regression model that gives the expected value of the dependent variable when all the independent variables equal zero

Mean Independent

The key requirement in multiple regression analysis that says the unobserved error has a mean that does not change across the subsets of the population defined by different values of the explanatory variables

OLS Regression Line

The equation relating the predicted value of the dependent variable to the independent variable, where the parameter estimates have been obtained by OLS

Ordinary Least Squares

A method for estimating the parameters of a multiple linear regression model. The ordinary least squares estimates are obtained by minimizing the sum of squared residuals

Population Regression Function

The expected or average value of one random variable, called the DV that depends on the values of one or more other variables, called the independent variable

Predicted Variable

Dependent Variable

Residual Sum of Squares (SSR)

Sum of Squared residuals

Response Variable

Dependent Variable

R-Squared

The proportion of the total sample variation in the dependent variable that is explained by the independent variable

Sample Regression Function

OLS regression Line

Semi-elasticity

the percentage change in the dependent variable given a one-unit increase in the independent variable

Simple Linear Regression Model

Model where the dv is a linear function of a single independent variable

Slope Parameter

the coefficient on an independent bariable in a multiple regression model

Standard Error of Beta 1 hat

Estimate of the standard deviation in the sampling distribution of Beta hat

Standard Error of the Regression

In multiple regression analysis, the estimate of the standard deviation of the population error, obtained as the square root o the sum of squared residuals over the degrees of freedom

Sum of Squared Residuals

sum of the squared OLS residuals across all observations

Total Sum of Squares

total sample variation in a dependent variable about its sample average

Zero Conditional Mean Assumption

given any values of the explanatory variables, the expected value of error equals zero