Demand Estimation - Regression Flashcards
What is regression analysis?
Regression analysis is a statistical technique used to estimate the relationships among variables, often to determine the demand function using data on quantity, price, income, and other variables.
How do you evaluate the statistical significance of estimated coefficients?
By using t-statistics and p-values. If the t-statistic is greater than 2 or the** p-value** is less than 0.05, the coefficient is considered statistically significant.
What is R-Square in regression analysis?
R-Square, or the coefficient of determination, measures the proportion of the variation in the dependent variable that is explained by the regression model. It ranges from 0 to 1.
What does the F-statistic indicate in regression analysis?
The F-statistic assesses whether the regression model has statistically significant explanatory power. If the significance F is less than 0.05, the model is considered significant.
What is multiple regression?
Multiple regression is a regression technique that involves more than one independent variable. It can be used to model nonlinear relationships and interactions between variables.
How do you interpret the regression results?
By examining the R-square, F-statistic, and the significance of individual coefficients. For example, a high R-square indicates a good fit, and significant coefficients suggest meaningful relationships between variables.
What does a negative coefficient for distance from campus indicate in a regression model for student property demand?
It indicates that as the distance from campus increases, the demand for student property units decreases.
What is the impact of price on demand according to the regression model?
A negative coefficient for price suggests that an increase in price leads to a decrease in the quantity demanded.
