Homework 5

library(dplyr)

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library(tidyverse)

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library(readxl)
Austin_crash_report <- read.csv("~/PAD 6833/Homework 5/Austin crash report.csv")

cor.test(Austin_crash_report$motor_vehicle_death_count,Austin_crash_report$crash_speed_limit,method="pearson")

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##  Pearson's product-moment correlation
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## data:  Austin_crash_report$motor_vehicle_death_count and Austin_crash_report$crash_speed_limit
## t = 9.4565, df = 161086, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.01867357 0.02843484
## sample estimates:
##        cor 
## 0.02355476

pairs(~crash_speed_limit+death_cnt+motor_vehicle_death_count+bicycle_death_count+pedestrian_death_count,data=Austin_crash_report)

cor.test(Austin_crash_report$`motor_vehicle_death_count`,Austin_crash_report$crash_speed_limit,method = "pearson")

## 
##  Pearson's product-moment correlation
## 
## data:  Austin_crash_report$motor_vehicle_death_count and Austin_crash_report$crash_speed_limit
## t = 9.4565, df = 161086, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.01867357 0.02843484
## sample estimates:
##        cor 
## 0.02355476

I utilized pesrson because it made the most sense to me as the data is continuous. I ran both kendall and spearman just to try out and was unable to successfully run the data. From what I interpret in this pearson correlation, the data is correlated in increased speed limit compared to vehicle death count. ```