Introduction of zoo data set

##  animal_name             hair           feathers          eggs       
##  Length:101         Min.   :0.0000   Min.   :0.000   Min.   :0.0000  
##  Class :character   1st Qu.:0.0000   1st Qu.:0.000   1st Qu.:0.0000  
##  Mode  :character   Median :0.0000   Median :0.000   Median :1.0000  
##                     Mean   :0.4257   Mean   :0.198   Mean   :0.5842  
##                     3rd Qu.:1.0000   3rd Qu.:0.000   3rd Qu.:1.0000  
##                     Max.   :1.0000   Max.   :1.000   Max.   :1.0000  
##       milk           airborne         aquatic          predator     
##  Min.   :0.0000   Min.   :0.0000   Min.   :0.0000   Min.   :0.0000  
##  1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000  
##  Median :0.0000   Median :0.0000   Median :0.0000   Median :1.0000  
##  Mean   :0.4059   Mean   :0.2376   Mean   :0.3564   Mean   :0.5545  
##  3rd Qu.:1.0000   3rd Qu.:0.0000   3rd Qu.:1.0000   3rd Qu.:1.0000  
##  Max.   :1.0000   Max.   :1.0000   Max.   :1.0000   Max.   :1.0000  
##     toothed         backbone     
##  Min.   :0.000   Min.   :0.0000  
##  1st Qu.:0.000   1st Qu.:1.0000  
##  Median :1.000   Median :1.0000  
##  Mean   :0.604   Mean   :0.8218  
##  3rd Qu.:1.000   3rd Qu.:1.0000  
##  Max.   :1.000   Max.   :1.0000

This is the summary of traits of animals from the zoo dataset pulled from archives.ics.uci.edu website. It contains 101 total animals with 17 biological attributes.

Code for Summary of Traits

zoo_summary <- zoo %>%
  group_by(type) %>%
  summarise(
      avg_legs = mean(legs),
      avg_hair = mean(hair),
      avg_predator = mean(predator)
  )

zoo_summary$type<- factor(
  zoo_summary$type,
  labels = c("Mammal", "Bird", "Reptile", "Fish",
             "Amphibian", "Bug", "Invertebrate")
)

Point Estimation of Means

\[\hat{\mu} = \frac{1}{n}\sum_{i=1}^{n} x_i\]

I used point estimation to estimate the true population mean, \(\hat{\mu}\), of a group of numeric traits on the animal types found from this zoo data set. These traits are hair, number of legs and predator status.

Estmated Point Mean of Number of Legs using ggplot

Used ggplot to plot a simple point plot of the average number of legs per animal type.

Estimated Point Mean of Hair in zoo Animals, (ggplot)

Estimated Point Mean of Predators in zoo Animals, (ggplot)

Point Estimate Math for zoo Traits

I used point estimation to estimate the true population proportion, \(p\). The formula: \[ \hat{p} = \frac{x}{n}.\] \(x\) is the number of animals per trait, \(n\) is the sample size and \(\hat{p}\) represents the estimated proportion for each trait.

zoo Trait Proportions with plotly

End of Slides

Thank you.