Wage = 10 + 1.2 × male + 2 × educ + 0.15 × (male × educ) + u %3D he dependent variable is hourly wage. There are three independent variables: a binary variable ndicating male, years of education, and an interaction between male and educ. a) Plot the relationship between wage and education for males and females separately. Put wage on the rertical axis and education on the horizontal axis. (hint: see Panel (b) of Figure 8.8 in the textbook; You nay draw the graph on software or on a piece of paper and paste the snapshot below.)

Wage = 10 + 1.2 × male + 2 × educ + 0.15 × (male × educ) + u %3D he dependent variable is hourly wage. There are three independent variables: a binary variable ndicating male, years of education, and an interaction between male and educ. a) Plot the relationship between wage and education for males and females separately. Put wage on the rertical axis and education on the horizontal axis. (hint: see Panel (b) of Figure 8.8 in the textbook; You nay draw the graph on software or on a piece of paper and paste the snapshot below.)

Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)

14th Edition

ISBN:9781305506381

Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Publisher:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Chapter4: Estimating Demand

Section: Chapter Questions

Problem 8E

See similar textbooks

Similar questions

Give typing answer with explanation and conclusion if education is heavily affected by IQ and IQ is closely correlated with both wage and education level, how would a regression of wage on education be biased? What regression violation is this? (For econometrics)
Consider 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?
Each cell represents a regression with a different outcome variable from Krueger’s (1993) study of workers who use computers. The left hand side variable is log wages. Answer based on column 2 of the above regression table. 1. Write a sentence that explains the relationship the regression is describing in row 2. 2. What is the number in parenthesis? What is it trying to communicate? 3. Write a causal statement that captures what the authors are trying to argue in row 2.
Investigate what factors determine the number of times a person logs into Facebook per week. It is argued that these four factors are important: number of friends, age in years, whether the person is employed, and whether the student has a Twitter account. That is: FACEBOOK LOGIN=f(FRIENDS,AGE,EMPLOYED,TWITTER) Do you think other relevant explanatory variables should also be included? Name any two such variables and explain why they should be included in the regression.
Regression analysis and the correlation coefficient can both be used to analyze the relationship between two economic variables. What are the advantages of using regression analysis instead of looking at the correlation coefficient?
does the simple regression of log (price) on log (nox) produce an upward or a downward biased estimator of β1?
We know that discrimination exists. It influences wages, but also many other dimensions over the life cycle which affect wages indirectly. I run OLS regression with variables wage, age, female and degree. The dependent variable is log(wage) and we replace the variables female and degree with the interaction term. However, discrimination is not included among the observed regressors. Given that omitting confounding variables from regression model can bias the coefficient estimates, omitting discrimination would lead to biased results. Could you please help me provide an example of how unobserved gender discrimination can affect my OLS estimates. [Hint: think about ways in which discrimination can invalidate OLS assumptions.].
Given the following data X (consumers of teff) or popn 3 6 8 1 13 13 14 Y ( teff consumption) 8 6 10 12 12 14 20 year 2013 2014 2015 2016 2017 2018 2019 Estimate the regression equation, Y= a+bX, Where Y denotes demand for teff while X is consumers of teff (population) By assuming demand for teff is only affected by its consumers, find the amount demand for teff in the year 2022 if the populations (consumers of teff) are about 18 people? (Hint: use the least square method, parameter a and b can be estimated by solving the two linear equations) SY= na+ bSX SXY=aSX +b Where n is number of years. For example, Estimate the sales for 2012, 2015 and fit a linear regression equation and draw a trend line.ar X Sales (Y) XY X2 year X Sales (Y) XY X2 2002 1 22734 22734 1 2003 2 24731 49462 4 2004 3 31489 94467 9 2005 4 44685 178740 16 2006 5 55319…
Run the Multiple Linear Regression using Fatalities as the dependentvariable and Licensed drivers, registered vehicles, and GDP per capita as independentvariables. Submit Excel FileCalculate the following:a- Interpret the R-squareb-Find the equation of the fitted linec- Which variable has the strongest relationship with number of fatalities?d- Identify which variables are significant/non-significant using alpha = 0.05? Fatalities Licensed Drivers Registered Vehicles GDP per Capita (measured) 953 3999057 5300199 40598 80 536033 803684 71996 1010 5284970 5806313 43464 516 2145334 2817145 38919 3563 27039400 31022328 68970 632 4244713 5356018 59885 294 2605612 2879802 68555 111 786504 1008468 64895 31 527731 351933 176498 3133 15368695 17496002 43423 1504 7168733 8512550 50288 117 948417 1267385 58185 231 1252535 1879670 40189 1031 8714788 10588910 60419 858 4589405 6190736 49209 318 2260271 3691892 54520 404 2149430 2684010 53094 724…
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…
Investigate what factors determine the number of times a person logs into Facebook per week. It is argued that these four factors are important: number of friends, age in years, whether the person is employed, and whether the student has a Twitter account. That is: FACEBOOK LOGIN=f(FRIENDS,AGE,EMPLOYED,TWITTER) Name two irrelevant explanatory variables that should not be included in the regression. Note that these two variables currently should NOT feature in the regression.
Question Three For the past 10 years, you have been observing the sales of your company since you embarked on an aggressive advertising campaign. You have been recording the amounts spent on advertising and the corresponding sales as follows: Year Advert (X) Sales (Y) 2001 10 44 2002 9 40 2003 11 42 2004 12 46 2005 11 48 2006 12 52 2007 13 54 2008 13 58 2009 14 56 2010 15 60 You would like to determine whether a relationship exists between your two variables of interest and therefore decide to run a regression. Specify the estimation model you will use (the econometric form). Identify the independent and the dependent variables. Use an econometric package of your choice (EViews, SPSS, STATA etc) to estimate the model. From the regression output, report the coefficients, standard errors, t-statistics, probability and R-squared (report the results in a table). Re-write the specified model in…