This assignment is due by midnight on Wednesday, June 2.
· Submit your assignment on Canvas as a pdf. You can write your answers up in a word document, save as pdf, and upload them onto R Exercise Module 1 right here.
· The main objective of this exercise is that you learn how to describe data in R.
Here is the “markdown” Rmd format of the assignment for those of you who are willing to experiment. You are not required to use it, but you may if you want:
R EXERCISE-MODULE1.Rmd download
Here is the database in case you have trouble opening the link from R:
Here is the description of the study that you need to solve question 2:
This case study is motivated by Harrison and Rubinfeld (1978), downloadwho proposed a hedonic model. for determining the willingness of house buyers to pay for clean air. This study employed data from the Boston metropolitan area, containing 560 suburbs and 14 variables. The dataset is available through the file housing_df.csv.
You can find the description of the related variables in the original paper, but I summarize here the most important ones. They are aggregated into five topics:
· Dependent variable: MEDV the median value of owner-occupied homes (in thousands of dollars).
· Structural variables indicating the house characteristics: RM (average number of rooms “in owner units”) and AGE (proportion of owner-occupied units built before 1940).
· Neighborhood variables: CRIM (crime rate), ZN (proportion of residential areas), INDUS( proportion of non-retail business area), CHAS (river limitation), TAX (cost of public services in each community), PTRATIO (pupil-teacher ratio), B (variable 1000( B- 0.63)2 . Where B the proportion of African American population – low and high values of B increase housing prices) and LSTAT (percent of the lower status of the population).
· Accessibility variables: DIS (distances to five Boston employment centers) and RAD (accessibility to radial highways – larger index denotes better accessibility).
· Air pollution variable: NOx, the annual concentration of nitrogen oxide (in parts per ten million).
Note: It is a fact that the market today, as in 1978, reflects some of the costs of racism. Let’s keep working so that there is a time when this variable (race) is not significant in housing price regressions!
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