*** Please use R studio to answer the following questions and provide R codes script. Thanks!***

The attached dataset (EVE.xlsx in text) is an anonymized random sample of proprietary data for users of the video game EVE Online. Eve is an online role-playing game hosted by the CCP Games. In EVE players create characters and then compete and interact with others in a fictional universe.

The dataset contains the following variables for a random sample of 311742 EVE characters from six real world nations:

• userID: an ID code for each unique user playing EVE
• UGender: the self-reported user gender
• UCountryIP: user nation, based on IP address
• Uage_decile: user self-reported age
• charID: an ID code for each unique character
• CGender: the character gender chosen by the player. This choice has no effect on the capabilities or resources of the character.
• CCareer: the character "career path" chosen by the player. This choice has no effect on the capabilities or resources of the character.
• CViolence: 1 = the character engages in violence during the first week of game play
• CCorp: 1 = the character joins a player run corporations
• CDMY: the month/year of character creation
• LogonHours: the number of hours of cumulative game play You have been hired as a consultant to advise CCP on tactics for increasing player engagement (LogonHours), as greater engagement presumably translates to increased subscriber revenue.

Please run R studio to answer follows questions:

• Using regression analysis, examine factors influencing engagement. (Use R studio)
• Report the results.
• Answer the sub-questions.
  • What factors are correlated with greater engagement?
  • How confident are you that nudging players towards certain choices (that is, prompting them to choose a military career or joining a corporation) will result in higher engagement?

*The following two screenshots are the sample codes and the link to the dataset *

https://docs.google.com/spreadsheets/d/1il0BsNZWBqCp22aw2_VEBp68l2ROLTem/edit?usp=sharing&ouid=109466379533506969270&rtpof=true&sd=true

simulated data # 1 - create the simulated data # total number of observations obs = mean(X2))] # dependent variable Y # Y is a function of X1 (b=.4) + X2 (b=.1) + random noise simdata[,Y := .4*X1 +.1*X2 + rnorm(obs, mean = 1, sd = .2 )] w