Consider the estimated regression model ŷ = 33.75 +1.45MALE where y is annual income in thousands and MALE is an indicator variable such that it takes the value of 1 for males and the value of 0 for females. According to this model, the average income for males is Select one: O a. $32,300 $33,750 Ob O C. $35,200 O d. $35.25 Clear my choice
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- 1. Let kids denote the number of children ever born to a woman, and let educ denote years of education for the woman. A simple model relating fertility to years of education is kids = 0 + 1educ + u, where u is the unobserved error. (a) What kinds of factors are contained in u? Are these likely to be correlated with level of education? (b) Will a simple regression analysis uncover the ceteris paribus eect of education on fertility? Explain.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)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…
- Suppose we use the linear probability model to estimate the effect of household income on the willingness to avail a loan product. Given a regression equation: Willingness = 22.34 -0.04HouseholdIncome, with HouseholdIncome measured in hundreds of pesos. How is -0.04 interpreted?What are 2 interpretations of the slope of a regression line?If we run a regression where y (bankruptcy) = f (factors potentially predicting bankruptcy), what is the dependent variable?
- You have data on all individuals in Sweden, including their family size (i.e. their number of children) and their earnings. You want to estimate the effect of family size on earnings, but you suspect your regression will be biased as you do not have information on preferences for how many children people want to have. a) You have information on gender of all children. You have heard that peoplewho have two children of the same gender are more likely to have a third child. Would you be able to use gender composition of the first two children as an instrument for family size? Discuss the assumptions needed for such a model (assume treatment effects are constant). Set up the assumptions mathematically and explain in words what they mean. b) How would you test if the assumptions in b) hold? Is it likely that they hold? (Assume gender composition of the first two children is the only instrument you have access to.)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.Consider the IV regression model Yi = β0 + β1Xi + β2Wi + ui, where Xi is correlated with ui and Zi is an instrument. Suppose that the first three assumptions in Key Concept (The IV Regression Assumptions) are satisfied. Which IV assumption is not satisfied whena) Zi is independent of (Yi, Xi, Wi)?b) Zi=Wi?c) Wi is1 for all i?d) Zi=Xi?
- 2. One topic from this module is heteroskedasticity.A. Convince me that you know what heteroskedasticity is.B. How can you use a scatterplot of the residuals from a regression model to look for heteroskedasticity?C. Explain briefly how to conduct a Park test for heteroskedasticity.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.].If you are interested in investigating themarginal effect of the percentage change of lot size on the changeof house price, then in your linear regression analysis you should apply thelogarithm transformation on: Select one: a. The house price variable. b. Both the house price variable and the lot size variable. c. The lot size variable. d. The bedroom variable. e. Both the bedroom variable and the house price variable.