IC11

Load the titanic_train.csv data set and see if you can create an ANOVA and Chi-Squared analysis from the data. Review slides 14 through 17 of Hypothesis testing for data science - regarding the R functions to use and assumptions.

##Use the categorical variables Pclass and Embarked in your analysis.

##Upload your zipped Rmd + HTML file or just your Rmd along with a link from RPubs.

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.2     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.2     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.1     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

setwd("C:/Users/StarKid/Desktop/Data_Science/Data_101/week_5/IC10")
titanic <- read.csv("titanic_train.csv")

str(titanic)

## 'data.frame':    891 obs. of  12 variables:
##  $ PassengerId: int  1 2 3 4 5 6 7 8 9 10 ...
##  $ Survived   : int  0 1 1 1 0 0 0 0 1 1 ...
##  $ Pclass     : int  3 1 3 1 3 3 1 3 3 2 ...
##  $ Name       : chr  "Braund, Mr. Owen Harris" "Cumings, Mrs. John Bradley (Florence Briggs Thayer)" "Heikkinen, Miss. Laina" "Futrelle, Mrs. Jacques Heath (Lily May Peel)" ...
##  $ Sex        : chr  "male" "female" "female" "female" ...
##  $ Age        : num  22 38 26 35 35 NA 54 2 27 14 ...
##  $ SibSp      : int  1 1 0 1 0 0 0 3 0 1 ...
##  $ Parch      : int  0 0 0 0 0 0 0 1 2 0 ...
##  $ Ticket     : chr  "A/5 21171" "PC 17599" "STON/O2. 3101282" "113803" ...
##  $ Fare       : num  7.25 71.28 7.92 53.1 8.05 ...
##  $ Cabin      : chr  "" "C85" "" "C123" ...
##  $ Embarked   : chr  "S" "C" "S" "S" ...

#INDEPENDENT 2 SAMPLE TEST

t.test(titanic$Fare, titanic$Age, conf.level = 0.95, alternative="greater")

## 
##  Welch Two Sample t-test
## 
## data:  titanic$Fare and titanic$Age
## t = 1.4304, df = 1074.7, p-value = 0.07644
## alternative hypothesis: true difference in means is greater than 0
## 95 percent confidence interval:
##  -0.37804      Inf
## sample estimates:
## mean of x mean of y 
##  32.20421  29.69912

DEPENDENT PAIR 2 SAMPLE T-TEST

t.test(titanic$Fare, titanic$Age, paired = TRUE)

## 
##  Paired t-test
## 
## data:  titanic$Fare and titanic$Age
## t = 2.4943, df = 713, p-value = 0.01285
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
##  1.063490 8.927303
## sample estimates:
## mean difference 
##        4.995396

qqnorm(titanic$Fare - titanic$Age)