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- da 13 Suppose that a researcher, by using a sample of transactions of residential houses, wants to estimate how the price of the houses is affected by the a non presence of an elevator in the building. To this end, he wants to regress the price of the house in thousands euro, price, on the dummy elevator indicating the presence of the elevator (dummy equal to 1) or not (dummy equal to 0). data Unfortunately, he makes a mistake so that he uses the original dummy divided by 100, so a variable that takes value 0.01 if there is an elevator in the building and 0 if not. agio max ontrassegna How the estimated value of the coefficient of the dummy will be affected by this mistake: nda O (a) It will be multiplied by 100 O (b) It will be divided by 100 O (c) It will change according to the proportion of flats in buildings with and without elevator O (d) None of the above Precedente SuccessivoEconomicsConsider data on every game played by the Brooklyn Nets in 2014 (82 games) that includes the variables ● marginį - the Net's margin of victory (number of points the Nets scored minus the number of points their opponent scored) for game i, and home; - a dummy variable equal to 1 when the Nets are the home team (game i was played in their home arena) and equal to O when they are the away team (game i was played in the opponent's arena). I use the least-squares method to estimate the following regression model margin, = a + ßhome; + ei Below is the Stata output corresponding to the estimated regression line: . regress margin home if team ==== "Brooklyn Nets". Source Model Residual Total margin home __cons SS 1459.95122 15252.0488 16712 df 1 80 Coef. Std. Err. MS 81 206.320988 8.439024 3.049595 -5.219512 2.156389 1459.95122 190.65061 t Number of obs F(1, 80) Prob > F R-squared P>|t| 2.77 0.007 -2.42 0.018 = Adj R-squared = Root MSE = 2.370137 -9.510864 = 82 7.66 0.0070 0.0874 0.0760 13.808…
- Please help me with both the question. Answer there are incorrectPlease show how to do b) and c). Quantity beef consumed is the dependent variable. Each of the others are the independent variables(Don't accept answers from Chat-GPT)You are estimating the following simple linear regression model: Edui = B0 + B1 MomEdu + ui. Where Edu is the years of schooling of an individual and MomEdu is the years of education of the individual's mother (Note: We might estimate this sort of regression to learn about intergenerational transmission of economic success.) a. Suppose you restrict your sample to individuals with MomEdui = 10 What happens to the OLS estimates? b. Suppose you have two random samples of size 100, both with the same In the first sample, half of the mothers have 12 yearsof education and half have 14 years of education. In the second sample, one quarter of of the mothers have each of 10, 12, 14. and 16 years of education. Does the variance of the OLS estimator differ between the two samples? Explain why or why not. C. Suppose you estimate the above regression using a random sample of 100 observations. Then you find another random sample of 100 with the same as the…
- Question 4 Suppose you have been hired by the government as an econometrician and have been tasked with predicting whether or not individuals will choose to get inoculated with a new vaccine developed to fight the next global pandemic. You have individual-level data from the previous pandemic on the following variables: ● ● Whether or not the individual chose to get vaccinated Age, gender, marital status, religion, education level and household income of each individual . A dummy for whether or not the individual was in the labor force Occupation, sector/industry and wage/earnings of each individual ● Develop an econometric model the government can use to predict whether or not an individual will choose to get vaccinated in the next global pandemic. State any assumptions you are relying on in constructing your model and justify the design of your model in the context of the problem faced. Specify the model mathematically, explain how you will estimate it (i.e. which estimation…An economist uses regression analysis to determine the relationship between used car price (y) and the age of a car (x). The analysis resulted in the following equation: Y = 30,000 - 500*X The above equation implies that an increase of O 1 year in the age of the car is associated with an increase of $500 in the price of the car O 1 year in the age of the car is associated with a decrease in $500 in the price of the car O $500 in the price of the car is associated with an increase of 5 years in the age of the car 5 years in the age of the car is associated with a decrease of $100 in the price of the car"In the regression model InY=b0+b1*InX+u, the coefficient b1 is interpreted as" O the intercept O A covariance O A regressor O An elasticity
- The following data from a random sample of individuals that are 25 years old. Note, wage is the hourly earnings in US dollars and educ is years of education. i 1 2 3 4 wage 12.18 12.18 20.09 7.39 educ 13 11 16 12 Consider the following model: In(wage)-Bo+B₁educ+u. Calculate the estimate of B₁. Round your answer to two decimal places.YOU CAN USE RThe following model studies human welfare using the General Social Survey in the US: happy = 0.014 + 0.209regattend + 0.103occattend + 0.027income + 0.009educ +0.003 female where happy is a dummy variable equal to one if a person is happy, and 0 otherwise: regattend is a dummy variable equal to one if a person regularly attends church, and 0 otherwise: occattend is a dummy variable equal to one if a person occasionally attends church, and 0 otherwise; income is family income in tens of thousands: educ is years of schooling: female = 1 if female. = 0 otherwise Interpret the impact of religion on happiness.
