BUSN5000 HW1 Part B

8/28/23, 9 : 33 PM Homework 1: Data Fundamentals Page 1 of 10 https://chris-cornwell.shinyapps.io/Assignment_1/#section-b.-empirical-exercise B. Empirical exercise In this exercise you will demonstrate basic knowledge about data structures and data documentation. Along the way, you will be introduced to a few R commands. You will do this using a data set constructed by David Card (https://davidcard.berkeley.edu/) for his well-known study analyzing the e ! ect of education on wages: Card, D., “Using Geographic Variation in College Proximity to Estimate the Return to Schooling” (https://davidcard.berkeley.edu/papers/geo_var_schooling.pdf), in Aspects of Labour Market Behavior: Essays in Honour of John Vanderkamp , E. Christophides, et al., eds, Toronto: University of Toronto Press (1995). These data will be featured prominently in Part II of the course when we will replicate some of Card’s analysis. For now, we’ll take the opportunity describe the key features of his data, as we would if we had conducted his analysis ourselves. The version of Card’s data we will use comes from the wooldridge package. You don’t have to worry about installing packages in this environment because that has been taken care of for you, but if you were replicating this exercise on your machine you would need to and here’s the command to do it: install.packages("wooldridge") install.packages("wooldridge") Then you would load the package using the library function: library() library() Once loaded, all of the package’s exported functions and objects become directly accessible in your R session. This means you can use those functions and objects as if they were part of the base R distribution without needing to reference the package name. Homework 1: Homework 1: Data Data Fundamentals Fundamentals A. Short answer (https://chris- cornwell.shinyapps.io/Assignment_1/#section- a.-short- answer) B. Empirical exercise (https://chris- cornwell.shinyapps.io/Assignment_1/#section- b.- empirical- exercise) Submission (https://chris- cornwell.shinyapps.io/Assignment_1/#section- submission) Start Over

8/28/23, 9 : 33 PM Homework 1: Data Fundamentals Page 2 of 10 https://chris-cornwell.shinyapps.io/Assignment_1/#section-b.-empirical-exercise Finally, you may want to explicitly load the Card data into your R environment. The Card data set is named card in the wooldridge package. (That’s card , all lower case. Case matters in R.) It’s generally not necessary to explicitly load the data with the data function a " er the relevant package is attached, but it will helpful with the project. Question 1: First, use the library() and data() functions to load the wooldridge package and card data set. There will be a few coding questions in the homework assignments that we need to grade so that you are on track to continue. This is one of them. So, before moving on make sure you click Submit Answer. If you have completed the code chunk correctly, you will get a “Correct” response in a green-shaded box below the chunk. Errors will be indicated in a red box. Provenance Before we look at the structure of the data, let’s do a little provenance work. (Just a little.) Go to the paper linked to above to answer the next six questions. Question 2: Card obtained the data from the _____. Card obtained the data from the _____. NLSYM Correct! Question 3: R Code ! Start Over ▶ Run Code ☑ Submit Answer I couldn’t have done it better myself. Correct! library(wooldridge) data(card) 1 2 3

8/28/23, 9 : 33 PM Homework 1: Data Fundamentals Page 4 of 10 https://chris-cornwell.shinyapps.io/Assignment_1/#section-b.-empirical-exercise Data Documentation and Structure Now let’s turn to documentation and structure. The wooldridge vignette (https://cran.r- project.org/web/packages/wooldridge/wooldridge.pdf) provides descriptions of the variables contained in the data set. Use the vignette to answer the next few questions. Question 7: The The key key variable in the data set is _____. variable in the data set is _____. id Correct! Question 8: The The wage wage variable is measured in _____. The variable is measured in _____. The lwage lwage variable variable is the _____ transformation of is the _____ transformation of wage wage . cents, log Correct! Question 9: The variable The variable exper exper measures labor-market experience as measures labor-market experience as ______. ______. education Incorrect Try Again The str() function, which provides an overview of the data type, size, and content in a data set. Apply it to determine the structure of the card data set and answer the questions that follow.

8/28/23, 9 : 33 PM Homework 1: Data Fundamentals Page 5 of 10 https://chris-cornwell.shinyapps.io/Assignment_1/#section-b.-empirical-exercise Question 10: The The card card data set contains _____ observations and _____ data set contains _____ observations and _____ variables. variables. 3010, 34 Correct! Question 11: R Code ! Start Over ▶ Run Code 'data.frame': 3010 obs. of 34 variables: 'data.frame': 3010 obs. of 34 variables: $ id : int 2 3 4 5 6 7 8 9 10 11 ... $ id : int 2 3 4 5 6 7 8 9 10 11 ... $ nearc2 : int 0 0 0 1 1 1 1 1 1 1 ... $ nearc2 : int 0 0 0 1 1 1 1 1 1 1 ... $ nearc4 : int 0 0 0 1 1 1 1 1 1 1 ... $ nearc4 : int 0 0 0 1 1 1 1 1 1 1 ... $ educ : int 7 12 12 11 12 12 18 14 12 12 $ educ : int 7 12 12 11 12 12 18 14 12 12 ... ... $ age : int 29 27 34 27 34 26 33 29 28 2 $ age : int 29 27 34 27 34 26 33 29 28 2 9 ... 9 ... $ fatheduc: int NA 8 14 11 8 9 14 14 12 12 . $ fatheduc: int NA 8 14 11 8 9 14 14 12 12 . .. .. $ motheduc: int NA 8 12 12 7 12 14 14 12 12 $ motheduc: int NA 8 12 12 7 12 14 14 12 12 ... ... $ weight : num 158413 380166 367470 380166 $ weight : num 158413 380166 367470 380166 367470 ... 367470 ... $ momdad14: int 1 1 1 1 1 1 1 1 1 1 ... $ momdad14: int 1 1 1 1 1 1 1 1 1 1 ... $ sinmom14: int 0 0 0 0 0 0 0 0 0 0 ... $ sinmom14: int 0 0 0 0 0 0 0 0 0 0 ... $ step14 : int 0 0 0 0 0 0 0 0 0 0 ... $ step14 : int 0 0 0 0 0 0 0 0 0 0 ... $ reg661 : int 1 1 1 0 0 0 0 0 0 0 ... $ reg661 : int 1 1 1 0 0 0 0 0 0 0 ... $ reg662 : int 0 0 0 1 1 1 1 1 1 1 ... $ reg662 : int 0 0 0 1 1 1 1 1 1 1 ... $ reg663 : int 0 0 0 0 0 0 0 0 0 0 ... $ reg663 : int 0 0 0 0 0 0 0 0 0 0 ... $ reg664 : int 0 0 0 0 0 0 0 0 0 0 ... $ reg664 : int 0 0 0 0 0 0 0 0 0 0 ... $ reg665 : int 0 0 0 0 0 0 0 0 0 0 ... $ reg665 : int 0 0 0 0 0 0 0 0 0 0 ... $ reg666 : int 0 0 0 0 0 0 0 0 0 0 ... $ reg666 : int 0 0 0 0 0 0 0 0 0 0 ... $ reg667 : int 0 0 0 0 0 0 0 0 0 0 ... $ reg667 : int 0 0 0 0 0 0 0 0 0 0 ... $ reg668 : int 0 0 0 0 0 0 0 0 0 0 ... $ reg668 : int 0 0 0 0 0 0 0 0 0 0 ... $ reg669 : int 0 0 0 0 0 0 0 0 0 0 ... $ reg669 : int 0 0 0 0 0 0 0 0 0 0 ... $ south66 : int 0 0 0 0 0 0 0 0 0 0 ... $ south66 : int 0 0 0 0 0 0 0 0 0 0 ... $ black : int 1 0 0 0 0 0 0 0 0 0 ... $ black : int 1 0 0 0 0 0 0 0 0 0 ... $ smsa : int 1 1 1 1 1 1 1 1 1 1 ... $ smsa : int 1 1 1 1 1 1 1 1 1 1 ... $ south : int 0 0 0 0 0 0 0 0 0 0 ... $ south : int 0 0 0 0 0 0 0 0 0 0 ... $ smsa66 : int 1 1 1 1 1 1 1 1 1 1 ... $ smsa66 : int 1 1 1 1 1 1 1 1 1 1 ... str(card) 1 2 3

8/28/23, 9 : 33 PM Homework 1: Data Fundamentals Page 7 of 10 https://chris-cornwell.shinyapps.io/Assignment_1/#section-b.-empirical-exercise skim_variable skim_variable n_missing n_missing complete_rate complete_rate mean mean sd sd p0 p0 id 0 1.00 2581.75 1500.54 2.00 nearc2 0 1.00 0.44 0.50 0.00 nearc4 0 1.00 0.68 0.47 0.00 educ 0 1.00 13.26 2.68 1.00 age 0 1.00 28.12 3.14 24.00 fatheduc 690 0.77 10.00 3.72 0.00 motheduc 353 0.88 10.35 3.18 0.00 weight 0 1.00 321185.26 170645.80 75607.00 momdad14 0 1.00 0.79 0.41 0.00 sinmom14 0 1.00 0.10 0.30 0.00 step14 0 1.00 0.04 0.19 0.00 reg661 0 1.00 0.05 0.21 0.00 reg662 0 1.00 0.16 0.37 0.00 reg663 0 1.00 0.20 0.40 0.00 reg664 0 1.00 0.06 0.25 0.00 reg665 0 1.00 0.21 0.41 0.00 reg666 0 1.00 0.10 0.29 0.00 reg667 0 1.00 0.11 0.31 0.00 reg668 0 1.00 0.03 0.17 0.00 reg669 0 1.00 0.09 0.29 0.00 south66 0 1.00 0.41 0.49 0.00 black 0 1.00 0.23 0.42 0.00 smsa 0 1.00 0.71 0.45 0.00 south 0 1.00 0.40 0.49 0.00 smsa66 0 1.00 0.65 0.48 0.00 wage 0 1.00 577.28 262.96 100.00

8/28/23, 9 : 33 PM Homework 1: Data Fundamentals Page 8 of 10 https://chris-cornwell.shinyapps.io/Assignment_1/#section-b.-empirical-exercise Question 13: How many variables have missing data? _____ . How many variables have missing data? _____ . 6 Correct! Question 14: What percentage of young men in the sample are missing What percentage of young men in the sample are missing IQ IQ test scores? _____ % . (Answer to 1 decimal place, for example: test scores? _____ % . (Answer to 1 decimal place, for example: ``99.9’’ percent) ``99.9’’ percent) 31.5 Correct! Question 15: What percentage of the sample are Black? _____ %. Is that What percentage of the sample are Black? _____ %. Is that representative of the US population in 1976? (Yes/No) _____ . representative of the US population in 1976? (Yes/No) _____ . 23, no Correct! Finally, use the object.size function to estimate the amount of memory allocated to store the Card data. enroll 0 1.00 0.09 0.29 0.00 KWW 47 0.98 33.54 8.61 4.00 IQ 949 0.68 102.45 15.42 50.00 married 7 1.00 2.27 2.07 1.00 libcrd14 13 1.00 0.67 0.47 0.00 exper 0 1.00 8.86 4.14 0.00 lwage 0 1.00 6.26 0.44 4.61 expersq 0 1.00 95.58 84.62 0.00

8/28/23, 9 : 33 PM Homework 1: Data Fundamentals Page 10 of 10 https://chris-cornwell.shinyapps.io/Assignment_1/#section-b.-empirical-exercise