One of the assumptions of the classical regression model is the following no explanatory variable is a perfect linear function of any other explanatory variables. Which of the following pairs of variables (X,,X,)would violate this assumption? O X = X Ex = "X O O X = X %3D O X = X %3D riolate this assumption
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- Discuss and explain each of the assumptions of the simple linear regression model.List the 5 assumptions of the Classical Linear Regression Model and explain at least three of themConsider the following estimated regression model relating annual salary to years of education and work experience. Estimated Salary=11,681.31+3418.97(Education)+1194.78(Experience) Suppose two employees at the company have been working there for five years. One has a bachelor's degree (8 years of education) and one has a master's degree (10 years of education). How much more money would we expect the employee with a master's degree to make?
- You are the owner of a restaurant located in a beach resort in Hawaii and want to use regression analysis to estimate the demand for your fresh seafood dinners. You have collected data on the daily quantity of seafood dinners sold over the last summer season. In order to correctly specify your regression equation, which of the following variables should be considered? Select one: A. the prices charged for souvenirs in local stores B. the prices charged for scuba diving excursions at the resort C. the wages paid to your chef and servers D. the daily number of vacationers at the resortWhich one of the following is NOT an assumption of the classical linear regression model (CLRM)? Select one: a. The disturbance terms are independent of one another. b. The dependent variable is not correlated with the disturbance terms. c. The explanatory variables are uncorrelated with the error terms. d. The disturbance terms have zero mean.What are the consequences in the regression results if multicollinearity is present in the regression model?
- Consider the regression model Yi = β0 + β1X1i + β2X2i + β3(X1i * X2i) +ui. a. ΔY>/ΔX1 = β1 + β3X2 (effect of change in X1, holding X2 constant).b. ΔY/ΔX2 = β2 + β3X1 (effect of change in X2, holding X1 constant).c. If X1 changes by ΔX1 and X2 changes by ΔX2, then ΔY =(β1+β3X2)ΔX1 + (β2 + β3X1)ΔX2 + β3ΔX1ΔX2.Define coefficients of the Linear Regression Model?Suppose you run a regression with quantity as your dependent variable and advertising as one of your independent variables. The p-value on advertising is .08. The marketing team is arguing that their advertising efforts are impacting sales, but the finance/economics department is arguing that there isn't evidence that the advertising is impacting sales. What side would you take and why? Note that this question squarely hits the idea that stats is part science/part art...
- Consider the following estimated regression model relating annual salary to years of education and work experience. Estimated Salary=10,160.10+3147.75(Education)+1230.34(Experience) Suppose an employee with 8 years of education has been with the company for 20 years (note that education years are the number of years after 8th grade). According to this model, what is his estimated annual salary?Suppose you run a regression with quantity as your dependent variable and advertising as one of your independent variables. The p-value on advertising is .08. The marketing team is arguing that their advertising efforts are impacting sales, but the finance/economics department is arguing that there isn't evidence that the advertising is impacting sales. What side would you take and why?Indicate whether the following statements are true, false or uncertain by providing necessary proofs. (a) Suppose your instructor told you to construct a model for demand for coffee by using income and the price of coffee as explanatory variables. However, for some reasons, you model the regression by using the price of coffee only. In that case your estimated regression may suggest that price of coffee has a positive effect on demand for coffee. (A proof is expected) (b) Consider the following estimated regression InY, = 2.57 + 0.212 InK + 0.343 InLe + 0.030 InW: (2.90) (0.348) (0.551) (0.063) where Y; is the industry output, K; and L represent capital and labor, respectively. It is claimed that there is no problem with the estimated regression and all the inferences are consistent with economic theory.