Estimating a Multiple Regression Model with R

Estimating a Multiple Regression Model with R The attached data, called wage2020 . csv contains 852 cross-sectional observations (11 = 852) for individuals who are earning monthly income in the labor force. The data set contains the following variables: earnings = monthly earnings in dollars hours = average weekly hours IQ = IQ score educ = years of education exper years of experience working tenure = years with current employer marrie 1 if married, 0 otherwise black 1 if black, 0 otherwise south 1 if live in Southern United States, 0 otherwise urban 1 if live in urban area, 0 otherwise sibs = number of siblings birthord = birthorder, 1 if first born, 2 if second born, etc. II II II 0- A. Estimate the multiple regression model using ordinary least squares with R described below. The dependent variable in your multiple regression model should be the natural logarithm of earnings, which can be computed using the log () function in R. Regress the natural logarithm of monthly earnings on a constant and all of the other variables listed above. R will automatically include a constant or intercept term. Use stargazer () to report the results in a well-formatted table as shown in the videos. Report the estimates and standard errors to the fifth decimal place in your regression table by using the digits=5 command with stargazer. B. Interpret the precise meaning of the estimated coefficient on married. What do you think might account for this? C. Is there evidence of labor market discrimination against Black Americans in this regression model? Explain. D. Suppose someone claims that coming from a large family, where you have lots of siblings, has a negative impact on labor market earnings. What are the null and alternative hypotheses? What can you conclude and why