Hello please help me answer these questions.

Here is the link for the data

https://docs.google.com/spreadsheets/d/1kQ_OOnhXFpFhDPBYYZQDRoOd2clwStqpaea_q4PoRnM/edit?usp=sharing

Instructions: Please submit one self-contained Word or PDF document that includes all of your calculations, written explanations, R scripts, and R output. It is due on Sunday, July 3 in Canvas before 10 p.m. as attachments in Assignments. You may also attach supplementary les, such as the actual R script les, etc. All of your work must be typed and well-formatted. No handwritten scans will be accepted, nor will late work. Submit what you have prior to 10 pm. The data for this problem set are in gendergapll 00 . csv and the data set consists of 1,100 observations from a random sample of individuals, and it includes the following dependent and explanatory variables: csv name Description Stargazer table label lnwage The natural logarithm of Natural Log of Wages hourly wages 1001's A measure of physical Physical Attractiveness attractiveness on a 1 to 5 scale (5=best) union 1 if a member of a labor Member of Labor Union union, and 0 otherwise. goodheal th 1 if person says they are in Good Health good health, and 0 otherwise. black 1 if person is black, and 0 Black otherwise. married 1 if person is married, and 0 Married otherwise. south 1 if person lives in South, and Lives in Southern U.S. 0 otherwise. bigcity 1 if person lives in a large Lives in a Big City city, and 0 otherwise. smallcity 1 if person lives in a small Lives in a Small City city, and 0 otherwise. service 1 if person works in a service Works in Service Sector sector occupation, and 0 Occupation otherwise. education The number of years of Years of Education formal education. female 1 if a person is female, and 0 Female otherwise. experience The number of years of work Years of Work Experience experience. female :experience Interactive duIILmv variable Female:Years of Work service 1 if person works in a service Works in Service Sector sector occupation, and 0 Occupation otherwise. education The number of years of Years of Education formal education. female 1 if a person is female, and 0 Female otherwise. experience The number of years of work Years of Work Experience experience. female: experience Interactive dumIny variable FemalezYears of Work (see Part 1 below) Experience 1. Estimate the Unrestricted Model with OLS Assume initially that the data-generating process (DGP) or true population regression function (PRF) is: lnwagei : .31 + ztooksi + B3union + goodhealthi + B5bIacki + Bmar'riedi + gsouthi + 33bigcityf + gsmallcityi + Bloservicei + lleducationi + lzemieriencei + 313 f female,- + 314 (female,- - experience) + .9,- Estimate the model using the 1m () command in R and report the rebults with the summary 0 command. Note that all of the variables are in the data frame, except the interactive durmny variable multiplying female and experience. You can use female : experience or female*experience inside your lmt) function to create this interactive variable. Name the unrestricted model in R as 01 su. 2. Interpreting Coefficients A. Precisely interpret the estimated coefficient on \"smallcity\". B. Precisely interpret the estimated coefficient on \"looks". 3. Testing a Linear Restriction Suppose you want to test for whether females have a different intercept and/or partial slope coefficient with regard to experience. As is standard, the null hypothesis is the "no effect\" or \"no difference\" hypothesis. So, in this case, the null hypothesis is: [10:313. : 14 : 0 First, do this test the \"long way" by saving the unrestricted residual sum of squares from Part 1. Then, impose the restriction above and re-estimate the model to get the restricted residual sum of squares. Third, compute the F- statistic using the unrestricted and restricted sum of squares from the unrestricted model (Part 1) and the restricted model (Part 3). lCompare this F- statistic to the appropriate F critical value or use R to get the p-value. Can you reject the null hypothesis above and accept the alternative hypothesis of a different intercept and/or partial slope for females at conventional signicance levels? Briey explain. Finally, use anova () to let R do the entire test with this one line of code to check your work. Jarque-B era Normality Test Use the results from Part 3 above to determine the "preferred\" model at this point. If you cannot reject the null hypothesis of the restriction, then use the restricted model in the tests below. If you reject the null hypothesis of the restriction, then use the unrestricted model in the tests below. Using R to conduct and interpret a Iarque-Bera (IB) normality test and graph the histogram of the residuals. What are your overall conclusions from this test? Explain. Estimate the Model and Report the Results with stargazer () Report the results in a well-labeled and formatted regression table using stargazer (J . Your stargazer command should begin with: stargazer (olsu, no . space=TRUE , style="ajps" , . ) The "....\" above are meant to show that you need to include other arguments within this stargazer function besides what I've shown above. The style is from the American journal of Political Science and it seems to format things well in this case. Use the labels for the dependent and explanatory variables given in the third column of the table of the variable descriptions on pp. 1-2 on this problem set, and add the title: \"Table 1: Multiple Regression Results". Finally, report all digits in your regression table to the 4* decimal place with digi ts=4. 3