Consider the two regression modelsModel (1):Pizza = Bi + BIncome + BAge + BAAge - Income +uModel (2): Pizza = Yi + y2Income + y3Age + uwherePizza - Individual's annual expenditure on pizzaIncome = Individual's annual incomeAge - Individual's age in yearsSuppose all the coefficient estimates are positive in both models.By including the interactive term in Model (1), what additional feature can we examine about therelationship between Pizza and Income and Age beyond what we can examine in the simpler Model(2)?O Whether the effect of Age on Pizza varies with Income,O If Income is higher, whether the predicted value of Pizza is higher, regardless of Age.O Whether Age and Income each have a positive influence on Pizza.O Whether Income tends to be larger as Age increases.O None of the presented possible answers are correct.

In statistics, regression analysis is a process for estimation of the relationships between the…

Answered: Consider the two regression models…

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

See similar textbooks

Similar questions

Being able to read regression results can help the manager use the information to make right decisions particularly in developing a marketing strategy. Assume that you are interested in finding whether the advertisement has a significant positive effect on sales. Which of the following is correct? A. lower standard errors of the estimates are better than higher standard errors B. as a rule of thumb, you are correct 95 % of the time in concluding that there is a positive and significant relationship between the advertising expenditures and sales if the coefficient attached to advertising expenditure is positive and the “t” value is at least 2 C. there is a positive significant relationship between advertising expenditure and sales if both the lower bound and the upper bound of the confidence interval are positive. D. the R2 shows the proportion of the variation in the sales as explained by the model which consists of the advertising expenditure plus some other determinants of sales…
What is the maximum weekly profit? What is the wholesale price per sleeping bag that should be charged to realize the maximum weekly profit?
1. Consider the following regression model y = x3 + u. (1) Let 3 denote the Ordinary Least Squares (OLS) estimator of B. The so-called Gauss- Markov assumptions are: • MLR.1: The true model in the population is given by (1). • MLR.2: We have a random sample of n observations {(ri, Yi), i = 1, 2, .., n} following the population model in (1). .... • MLR.3: No one explanatory variable can be written as a linear combination of the remaining explanatory variables that is, there is no perfectcollinearity. • MLR.4: In the population, the error u has an expected value of zero given any values of the explanatory variables, that is Elu|x] = 0. • MLR.5: In the population, the error u has the same variance given any values of the explanatory variables, that is Var[u|x] = o? , an unknown finite, positive constant. In the following scenarios, state whether B is an unbiased and consistent estimator of 3, and provide a brief justification for your answer in each case - but no formal mathematical…
b. The following model is a simplified version of the multiple regression model used by BEST Econometrics Group to study the trade off between time spent sleeping and working to look at other factors affecting sleep: sleep Bo + B₁totwrk + B₂educ + Page + μ₁ where sleep and totwork (total work) are measure in minutes per week and educ and age are measured in years. (i) If adults trade sleep for work, what is the sign of B₁? (ii) What signs do you think ₂ and 3 will have? (iii) Using the data in SLEEP75.RAW, the estimated equation is sleep = 3,638.25-.148totwrk-11.13educ - 2.20age, n = 706, R² = .113 If someone works five hours per week, by how many minutes is sleep predicted to fall? Is this a large tradeoff? (iv) Discuss the sign and magnitude of the estimated coefficient on educ (v) Would you say totwrk, educ, and age explain much of the variation in sleep? (vi) What other factors might affect the time spent sleeping? (vii) Are these likely to be correlated with totwrk?
Question 3 Consider the table below with regression output from two OLS regressions estimating the share of household budget devoted to food and adult goods (tobacco and alcohol) in some country using the Working-Leser Engel curve specification: K-1 w; = a; + B,ln () + niln(n)+ E Vik + T;z + ui k=1 where w; is the share of total household expenditure on good i (either food or tobacco and alcohol) measured as a proportion between 0 and 1, y is total household expenditure, n is household size, n; e {1, .., K} is the number of people in age-sex category j, z is a vector of other household characteristics, and u is an error term for the ith good. Assume that all coefficients are statistically significant. Table 1: OLS regression coefficients of household share of expenditure spent on food and adult goods Food Tobacco and Alcohol In (4) -0.70 -0.33 In(n) -0.02 0.12 Ratio of males: 0-2 years -1.46 -0.42 3-4 years -1.63 -0.12 5-14 years -4.78 -0.17 15-54 years -4.63 0.57 55+ years -5.11 0.87…
q4-
We estimate a simple regression explaining monthly salary (salary) in terms of IQ score (IQ), using data from a random sample of 935 individuals. We obtain the following estimated regression line: salary = 117 + 8.30 × IQ What is the correct interpretation of the estimated slope coefficient? Individuals with IQ scores of 100 have, on average, monthly wages of $830. An additional one point increase in IQ score is associated, on average, with an increase in monthly salary of $8.30. An additional one point increase in IQ score is associated, on average, with a decrease in monthly salary of $8.30. Each additional one point increase in IQ score will cause an increase in an individual's monthly salary by $8.30.
13. Collinearity in a multiple regression analysis Suppose you want to examine the effects of a training program on future earnings using the following model: earn98= 4.64 +2.376train +0.371earn96 +0.366educ- 1.86 age +2.534 married (1.14) (0.43) (0.016) (0.062) (0.013) (0.4) where earn 98- 1998 earnings, in thousands of dollars train -1 if the individual participated in the training program, and =0 otherwise earn 96- 1996 earnings, in thousands of dollars educ years of education age = age, in years married-1 if the individual is married, and -0 otherwise Suppose that there is a high degree of correlation (but not perfect) between earnings in 1996, education, age, and marital status. True or False: We should be concerned about this high degree of correlation because it affects our ability to reliably estimate the impact of the training program on 1998 earnings, T. True False
number 1 please
How do you interpret the R-squared obtained from running this regression?
In multiple OLS regressions, if you are using power terms to fit for nonlinearity, how do you interpret the coefficients? For example: Yi=B1+B2X+B3X^2+Ui and B2 and B3 are both significant.
Consider a regression in which a coefficient suffers from downward omitted variable bias. If you add a regressor that controls for the omitted variable bias: Group of answer choices 1. Can’t determine 2. The new estimate will be smaller. 3. The new estimate will remain unchanged. 4. The new estimate will be larger

SEE MORE QUESTIONS

Recommended textbooks for you

ENGR.ECONOMIC ANALYSIS

Economics

ISBN:

9780190931919

Author:

NEWNAN

Publisher:

Oxford University Press

Principles of Economics (12th Edition)

Economics

ISBN:

9780134078779

Author:

Karl E. Case, Ray C. Fair, Sharon E. Oster

Publisher:

PEARSON

Engineering Economy (17th Edition)

Economics

ISBN:

9780134870069

Author:

William G. Sullivan, Elin M. Wicks, C. Patrick Koelling

Publisher:

PEARSON

ENGR.ECONOMIC ANALYSIS

Economics

ISBN:

9780190931919

Author:

NEWNAN

Publisher:

Oxford University Press

Principles of Economics (12th Edition)

Economics

ISBN:

9780134078779

Author:

Karl E. Case, Ray C. Fair, Sharon E. Oster

Publisher:

PEARSON

Engineering Economy (17th Edition)

Economics

ISBN:

9780134870069

Author:

William G. Sullivan, Elin M. Wicks, C. Patrick Koelling

Publisher:

PEARSON

Principles of Economics (MindTap Course List)

Economics

ISBN:

9781305585126

Author:

N. Gregory Mankiw

Publisher:

Cengage Learning

Managerial Economics: A Problem Solving Approach

Economics

ISBN:

9781337106665

Author:

Luke M. Froeb, Brian T. McCann, Michael R. Ward, Mike Shor

Publisher:

Cengage Learning

Managerial Economics & Business Strategy (Mcgraw-…

Economics

ISBN:

9781259290619

Author:

Michael Baye, Jeff Prince

Publisher:

McGraw-Hill Education

SEE MORE TEXTBOOKS