Final Project

Load in the required packages:

library(tidyverse)

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library(rstatix)

## 
## Attaching package: 'rstatix'
## 
## The following object is masked from 'package:stats':
## 
##     filter

Load in the dataset:

file.choose()

## [1] "X4S\xa6\x9ed"

project<-read.csv("/cloud/project/Christopher Bealer/Final/Wigginton_Data1.csv", stringsAsFactors = TRUE)
project

What analysis will you run? Correlation/Regression

light_reg<-lm(Invasive.Plant.Percent.Cover~Light.Penetration, data=project)
summary(light_reg)

## 
## Call:
## lm(formula = Invasive.Plant.Percent.Cover ~ Light.Penetration, 
##     data = project)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -11.359  -6.714  -2.214   5.785  17.356 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        116.662      4.894   23.84 4.11e-12 ***
## Light.Penetration -120.405      8.769  -13.73 4.09e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.478 on 13 degrees of freedom
## Multiple R-squared:  0.9355, Adjusted R-squared:  0.9305 
## F-statistic: 188.5 on 1 and 13 DF,  p-value: 4.088e-09

Strong negative correlation; reject the null

light_res<-residuals(light_reg)

shapiro.test(light_res)

## 
##  Shapiro-Wilk normality test
## 
## data:  light_res
## W = 0.90463, p-value = 0.112

Report all the statistics:

p-value=4.088e-09; strong negative correlation r-squared=0.94 p-value(shapiro)=0.112, association present

What is your conclusion from the analysis?

According to the regression analysis, we reject the null hypothesis (p=4.088e-09) and conclude that there is a negative relationship between light penetration levels and the amount of invasive plant species cover. There is high amounts of variability around the model (r-squared=0.9355; y=-120.41x+166.67).

Create a data visualization and figure caption:

ggplot(project, aes(x=Light.Penetration, y=Invasive.Plant.Percent.Cover))+
  geom_point()+
  geom_smooth(method="lm", se=FALSE, color="black")+
  theme_classic(16)+
  xlab("Light Penetration")+
  ylab("Plant Cover (%)")

## `geom_smooth()` using formula = 'y ~ x'

Great! Copy and paste your data visualization to your word document or save the image.