setwd("/Users/teddy/OneDrive - Endicott College/Desktop/R data")
project_data<-read.table('Burt_CC-DiveSurveys2013-2016_Site_Sum_stats.csv',sep=',', header=T)
project_data$Otter<-factor(project_data$Otter, levels=c(0,1), labels=c("No", "Yes") )
#Two sample Ttest
t.test(AvgBiom5 ~ Otter, data = project_data, conf.level = 0.95, alternative = "two.sided", var.equal = T)
##
## Two Sample t-test
##
## data: AvgBiom5 by Otter
## t = 14.814, df = 42, p-value < 2.2e-16
## alternative hypothesis: true difference in means between group No and group Yes is not equal to 0
## 95 percent confidence interval:
## 1.475917 1.941462
## sample estimates:
## mean in group No mean in group Yes
## 1.8880000 0.1793103
boxplot(AvgBiom5 ~ Otter,
data = project_data)
#Regression
project_data.lm <- lm(AvgBiom5 ~ Pycno, data = project_data)
coef(project_data.lm)
## (Intercept) Pycno
## 0.6124930 0.3714138
summary(project_data.lm)
##
## Call:
## lm(formula = AvgBiom5 ~ Pycno, data = project_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9703 -0.6363 -0.3051 0.3995 2.0164
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.6125 0.1632 3.753 0.00053 ***
## Pycno 0.3714 0.2367 1.569 0.12414
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8795 on 42 degrees of freedom
## Multiple R-squared: 0.05537, Adjusted R-squared: 0.03288
## F-statistic: 2.462 on 1 and 42 DF, p-value: 0.1241
plot(x = project_data$Pycno, y = project_data$AvgBiom5, pch = 16, col="blue")
abline(project_data.lm, col ='red')
#ANOVA
your_model <- aov(AvgBiom5 ~ Predscen,
data = project_data)
summary(your_model)
## Df Sum Sq Mean Sq F value Pr(>F)
## Predscen 3 30.213 10.071 96.48 <2e-16 ***
## Residuals 40 4.175 0.104
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#assumptions
library(car)
## Loading required package: carData
leveneTest(AvgBiom5 ~ Predscen,
data = project_data)
## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 3.7453 0.01838 *
## 40
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Shapiro Wilk's Test
shapiro.test(project_data$AvgBiom5)
##
## Shapiro-Wilk normality test
##
## data: project_data$AvgBiom5
## W = 0.78186, p-value = 1.204e-06
#Results
TukeyHSD(your_model)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = AvgBiom5 ~ Predscen, data = project_data)
##
## $Predscen
## diff lwr upr p adj
## none-both 2.1912308 1.735516 2.6469453 0.0000000
## OTTonly-both 0.1604808 -0.162875 0.4838366 0.5495581
## PYConly-both 1.6002308 1.235976 1.9644858 0.0000000
## OTTonly-none -2.0307500 -2.474438 -1.5870617 0.0000000
## PYConly-none -0.5910000 -1.065323 -0.1166773 0.0094809
## PYConly-OTTonly 1.4397500 1.090658 1.7888421 0.0000000
boxplot(AvgBiom5 ~ Predscen,
data = project_data)
#ANOVA
Kelp_model <- aov(AvgKelp ~ Predscen,
data = project_data)
summary(Kelp_model)
## Df Sum Sq Mean Sq F value Pr(>F)
## Predscen 3 305.3 101.8 21.66 1.71e-08 ***
## Residuals 40 188.0 4.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1