Publications by Mauricio Rodriguez Abreu
LAT4082_S10
R Markdown Read the file on Claim Levels setwd("~/Dropbox/UDLAP/Cursos/2022 Primavera/Tema Selecto/Presentaciones") ClaimLev <- read.csv("CLAIMLEVEL.csv") head(ClaimLev) ## PolicyNum ClaimNum Year ClaimStatus Claim Deduct EntityType ## 1 120002 20100192 2010 Closed 6838.87 1000 County ## 2 120003 20080726 2007 Closed 20...
710 sym R (4315 sym/44 pcs) 18 img
LAT4072_ENOE_PartB
R Markdown In this session, we will learn how to use the ENOE, a really important survey collecting information on employment and occupations. This survey is the one you will have to use for your final project. setwd("~/Downloads/enoe_n_2021_trim1_csv") sdem<-read.csv("ENOEN_SDEMT121.csv") Remember, my advice is to always start with a basic anali...
5186 sym R (29460 sym/119 pcs)
LAT4082_S13
Now, we will lear how to use GLM’s to fit a model for claim frequency. We will be working with the same datase from last session, so I will start by loading the file: setwd("~/Dropbox/UDLAP/Cursos/2022 Primavera/Tema Selecto/Presentaciones") data<-read.csv("freMTPLfreq.csv") Just to remember the variables we had: names(data) ## [1] "X" ...
4656 sym R (15627 sym/42 pcs)
LAT4072_S18B
R Markdown setwd("~/Dropbox/UDLAP/Cursos/2022 Primavera/Pensiones y SS/Presentaciones") ciencias<-read.csv("ciencias.csv") Now, we define a benefit the following way: Retirement age: 60 Benefit: \[ 1.5\% \times SP \times AS \] Where \(SP\) is the pensionable salary and \(AS\) represents the years of service. Pensionable salary is the average of ...
1823 sym R (4799 sym/39 pcs)