Clears all R memory rm(list=ls())

Generating our data:

In groups create a table with the following information: first name, last name, sex, country of birth, and city of birth data from your group as 5 different character vectors. Use the following names: first_name, last_name, sex, country_of_birth, city_of_birth.

First_name of my group - Create a character vector called first_name containing the names of your group.

first_name <- c("Claudia", "Maddie", "Lisa", "Adriana")
first_name
## [1] "Claudia" "Maddie"  "Lisa"    "Adriana"

Last_name of my group - Create character vector called last_name containing the last names of your group.

last_name <- c("Cuellar", "Lews", "Dwiananda", "Salazar")
last_name
## [1] "Cuellar"   "Lews"      "Dwiananda" "Salazar"

Age of my group - Create a vector called age containing the ages of your group.

age <- c(27,21,21,24)
age
## [1] 27 21 21 24

Height of my group - Create a vector called height containing the ages of your group.

height <- c(160,157, 150,156)
height
## [1] 160 157 150 156

Sex of my group - Create a character vector called sex containing the sex of your group.

sex <- c("Female", "Female","Female","Female")
sex
## [1] "Female" "Female" "Female" "Female"

City_of_birth of my group - Create a character vector called country_of_birth containing the ages of your group.

country_of_birth <- c("Mexico", "Wales", "Indonesia", "Mexico")
country_of_birth
## [1] "Mexico"    "Wales"     "Indonesia" "Mexico"

Country_of_birth of my group -Create a character vector called city_of_birth containing the TRUE/FALSE data you collected.

city_of_birth <- c("Mexico", "Cardiff", "Jakarta", "Mexico")
city_of_birth
## [1] "Mexico"  "Cardiff" "Jakarta" "Mexico"

Played_cricket - Create a logical vector called played_cricket containing TRUE/FALSE from your group.

played_cricket <- c(FALSE, FALSE, FALSE, FALSE)
played_cricket
## [1] FALSE FALSE FALSE FALSE

Create a data.frame with the information of your group.

table2 <- data.frame(first_name, last_name, age, height, sex,country_of_birth,city_of_birth,played_cricket)

table2
##   first_name last_name age height    sex country_of_birth city_of_birth
## 1    Claudia   Cuellar  27    160 Female           Mexico        Mexico
## 2     Maddie      Lews  21    157 Female            Wales       Cardiff
## 3       Lisa Dwiananda  21    150 Female        Indonesia       Jakarta
## 4    Adriana   Salazar  24    156 Female           Mexico        Mexico
##   played_cricket
## 1          FALSE
## 2          FALSE
## 3          FALSE
## 4          FALSE

Calculate the standard error of height in your group.

Calculation of standard error with = sd(data) / sqrt(length(data))

std <- sd(height) / sqrt(length(height))
std
## [1] 2.096624

Calculate the standard error of height with a function

se <- function(height){
  return(sd(height)/sqrt(length(height)))
}

standard_error <- se(table2$height)
standard_error
## [1] 2.096624

Calculate the standard error of age with a function

sa <- function(age){
  return(sd(age)/sqrt(length(age)))
}

standard_errora <- sa(table2$age)
standard_errora
## [1] 1.436141