1. Which of these is regression not used for? Sensitization
2. What is regression used for? Prediction, description, and control
3. OLS Regression minimizes? Sum of squared errors
4. In the linear regression equation, what is the name of the ei term? Error
5. In the linear regression equation, what is the name of the B term? Coefficient of slope
6. What is the name of the alpha term in the equation? Intercept
7. R^2? Shows % of variation in Y explained by X
8. What does the F in the ANOVA box test? Whether all coefficients are zero
9. Which of these is Not an assumption of linear regression? Only one x variable
10. Suppose we add an IV called working (Yes,No) to a regression. We would use which kind
of variable? Dummy
11. If you have categorical variable with three categories, how mnay dummy variables
should you make? 2
12. Price elasticity of demand: having a coefficient <-1 is? Elastic
13. Y_hat = 50 + 25*, Y_hat is eastimated sales, X is advertising When x = 3 the estimated
sales are 125.
14. Y_hat = 50 + 50x? The predicted level of y, if x=0, is 50 and for each unit increase in x, y
will increase by 50
15. The “null” model for a regression? Fits the interceot and is relevant to R^2
16. Adding an interaction term to a regression onto a continuous and dummy variable?
Allows the effect of continuous var to vary by dummy var
17. Suppose youre predicting sales from 40-10*price+0.5*advertising? When price = 2 ,
advertising = 5-, then prediction = 45.
18. What is the dependent variable? Yi=
19. Whats the intercept? Yi=(A)+
20. What is or are the independent variables? X2i and X1i
21. The error term is assumed to? Be normally distributed
22. Rhe F ratio in the regression output? Test against a null of betas = 0
23. R^2 comparess the full model to ? null model
24. Adding more variables to the regression? Boosts R^2 and allows you to control more
variables
25. A log-log regression? Provides change in % instead of unit
26. In a log-log model, when the coefficient is <0
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