BIN510_Unit2_Exam
Gordon Ober
2024-07-22
For this exam you will be using the file “tick.csv” found on canvas:
Ticks use large mammals as hosts and the presence of mammals represents an important vector for tick-borne diseases and humans. Tick presence may also be influenced by environmental factors. The tick.csv file has a number of different variables that can be analyzed, below are a list of the important variables for this study:
Treatment - what type of animals were allowed within the plot? CONT (control, no exclosure), LMH (total exclosure, no mammals), MESO (exclosion of large herbivores >15kg), and MEGA (exclusion of Elephants and Giraffes)
Rain - average rainfall (mm/year)
Total - total number of ticks found across 3 study species
PART 1a: How does treatment of mammals influence the total number of ticks found? Answer the questions below:
NOTE: for each prompt, I need to see code in order to give you credit! NOTE: you can perform all of these tasks in one code chunk or many code chunks, this is up to you
Code: bring in the dataset and name it “tick”
Question: what type of test would be appropriate to run in order to compare total ticks by treatment type? Why?
- An ANOVA test would be appropriate as there is one categorical variable (which has more than two levels), and a numerical variable.
- Question: what assumptions need to be tested before running the test?
- We need to test sample normality, and is samples have similar standard deviations of groups (equal variance).
Code: test all assumptions from 3
Question: interpret the results of 4. (tell me if assumptions are met or not) a.The Shapiro test gave a p-value of 2.2e-16. So the sample data is not from a population with normal distribution. b.The levene test gave a p-value of 9.346e-05. So the sample data is not from a population with equal variance.
Code: Regardless of your answer to 5*, run the test you propose in 2
Question: what does the output of 6 tell you about ticks and treatment?
- The output of 6 was a p-value of 2.13e-05. This tells us to reject the null. Ticks are influenced by treatment. At least one group is different than the others.
- Question: do you need to run post-hoc analysis? Why or why not?
- Yes, a post-hoc needs to be run. Since we rejected the null that means at least one treatment group is different than the others and the post-hoc analysis will tell us which one(s).
Code: if you answered YES to 8, run that analysis here
Question: if you ran 9, tell me what the output tells us about the treatments
- The output shows that the following are significantly different from each other in impacting total tick each with p-values >0.05: -MEGA-CONT -MESO-CONT -MESO-MEGA
- Create a visual that helps tell the story of how treatment influences ticks
tick <- read.table('ticks.csv', ',', header=T)
tick## Period Date Month Level Replicate Treatment Total RHPU RHPR RHPV
## 1 11.0 20/08/2014 Aug 2 4 CONT 1 0 0 1
## 2 11.0 20/08/2014 Aug 2 5 CONT 1 0 0 1
## 3 11.0 20/08/2014 Aug 2 6 CONT 1 0 0 1
## 4 11.0 21/08/2014 Aug 3 7 CONT 0 0 0 0
## 5 11.0 21/08/2014 Aug 3 8 CONT 0 0 0 0
## 6 11.0 21/08/2014 Aug 3 9 CONT 5 4 0 1
## 7 11.0 22/08/2014 Aug 1 1 CONT 4 3 0 1
## 8 11.0 22/08/2014 Aug 1 2 CONT 0 0 0 0
## 9 11.0 22/08/2014 Aug 1 3 CONT 1 0 0 1
## 10 11.0 20/08/2014 Aug 2 4 LMH 37 0 1 36
## 11 11.0 20/08/2014 Aug 2 5 LMH 5 0 1 4
## 12 11.0 20/08/2014 Aug 2 6 LMH 13 1 1 11
## 13 11.0 21/08/2014 Aug 3 7 LMH 45 0 2 43
## 14 11.0 21/08/2014 Aug 3 8 LMH 10 3 0 7
## 15 11.0 21/08/2014 Aug 3 9 LMH 9 2 0 7
## 16 11.0 22/08/2014 Aug 1 1 LMH 4 0 3 1
## 17 11.0 22/08/2014 Aug 1 2 LMH 5 1 4 0
## 18 11.0 22/08/2014 Aug 1 3 LMH 0 0 0 0
## 19 11.0 20/08/2014 Aug 2 4 MEGA 17 3 1 13
## 20 11.0 20/08/2014 Aug 2 5 MEGA 7 3 0 4
## 21 11.0 20/08/2014 Aug 2 6 MEGA 6 1 0 5
## 22 11.0 21/08/2014 Aug 3 7 MEGA 2 0 0 2
## 23 11.0 21/08/2014 Aug 3 8 MEGA 1 0 0 1
## 24 11.0 21/08/2014 Aug 3 9 MEGA 1 0 0 1
## 25 11.0 22/08/2014 Aug 1 1 MEGA 5 1 2 2
## 26 11.0 22/08/2014 Aug 1 2 MEGA 2 1 0 1
## 27 11.0 22/08/2014 Aug 1 3 MEGA 6 6 0 0
## 28 11.0 20/08/2014 Aug 2 4 MESO 28 5 0 23
## 29 11.0 20/08/2014 Aug 2 5 MESO 14 12 1 1
## 30 11.0 20/08/2014 Aug 2 6 MESO 8 8 0 0
## 31 11.0 21/08/2014 Aug 3 7 MESO 8 5 0 3
## 32 11.0 21/08/2014 Aug 3 8 MESO 15 13 0 2
## 33 11.0 21/08/2014 Aug 3 9 MESO 10 7 0 3
## 34 11.0 22/08/2014 Aug 1 1 MESO 4 0 1 3
## 35 11.0 22/08/2014 Aug 1 2 MESO 2 2 0 0
## 36 11.0 22/08/2014 Aug 1 3 MESO 0 0 0 0
## 37 4.0 27/01/2014 Jan 2 4 CONT 1 0 0 1
## 38 4.0 17/01/2014 Jan 2 5 CONT 14 11 3 0
## 39 4.0 27/01/2014 Jan 2 6 CONT 26 0 21 5
## 40 4.0 20/01/2014 Jan 3 7 CONT 8 6 0 2
## 41 4.0 25/01/2014 Jan 3 8 CONT 4 3 0 1
## 42 4.0 20/01/2014 Jan 3 9 CONT 5 5 0 0
## 43 4.0 14/01/2014 Jan 1 1 CONT 14 3 11 0
## 44 4.0 14/01/2014 Jan 1 2 CONT 4 1 3 0
## 45 4.0 18/01/2014 Jan 1 3 CONT 2 2 0 0
## 46 4.0 27/01/2014 Jan 2 4 LMH 4 0 0 4
## 47 4.0 17/01/2014 Jan 2 5 LMH 2 2 0 0
## 48 4.0 27/01/2014 Jan 2 6 LMH 8 4 0 4
## 49 4.0 20/01/2014 Jan 3 7 LMH 8 0 0 8
## 50 4.0 25/01/2014 Jan 3 8 LMH 3 0 0 3
## 51 4.0 20/01/2014 Jan 3 9 LMH 19 1 0 18
## 52 4.0 14/01/2014 Jan 1 1 LMH 38 0 38 0
## 53 4.0 14/01/2014 Jan 1 2 LMH 25 0 25 0
## 54 4.0 18/01/2014 Jan 1 3 LMH 0 0 0 0
## 55 4.0 27/01/2014 Jan 2 4 MEGA 16 3 12 1
## 56 4.0 17/01/2014 Jan 2 5 MEGA 9 6 3 0
## 57 4.0 27/01/2014 Jan 2 6 MEGA 0 0 0 0
## 58 4.0 20/01/2014 Jan 3 7 MEGA 11 2 7 2
## 59 4.0 25/01/2014 Jan 3 8 MEGA 19 13 2 4
## 60 4.0 20/01/2014 Jan 3 9 MEGA 21 13 3 5
## 61 4.0 14/01/2014 Jan 1 1 MEGA 21 0 21 0
## 62 4.0 14/01/2014 Jan 1 2 MEGA 14 3 11 0
## 63 4.0 18/01/2014 Jan 1 3 MEGA 16 2 14 0
## 64 4.0 27/01/2014 Jan 2 4 MESO 17 0 17 0
## 65 4.0 17/01/2014 Jan 2 5 MESO 10 3 6 1
## 66 4.0 27/01/2014 Jan 2 6 MESO 9 5 3 1
## 67 4.0 20/01/2014 Jan 3 7 MESO 34 8 25 1
## 68 4.0 25/01/2014 Jan 3 8 MESO 13 6 6 1
## 69 4.0 20/01/2014 Jan 3 9 MESO 19 7 5 7
## 70 4.0 14/01/2014 Jan 1 1 MESO 16 0 16 0
## 71 4.0 14/01/2014 Jan 1 2 MESO 3 0 3 0
## 72 4.0 18/01/2014 Jan 1 3 MESO 5 1 4 0
## 73 10.0 23/07/2014 Jul 2 4 CONT 7 0 1 6
## 74 10.0 23/07/2014 Jul 2 5 CONT 11 11 0 0
## 75 10.0 23/07/2014 Jul 2 6 CONT 2 0 0 2
## 76 10.0 24/07/2014 Jul 3 7 CONT 10 0 0 10
## 77 10.0 24/07/2014 Jul 3 8 CONT 0 0 0 0
## 78 10.0 24/07/2014 Jul 3 9 CONT 2 0 0 2
## 79 10.0 22/07/2014 Jul 1 1 CONT 4 0 0 4
## 80 10.0 22/07/2014 Jul 1 2 CONT 0 0 0 0
## 81 10.0 22/07/2014 Jul 1 3 CONT 0 0 0 0
## 82 10.0 23/07/2014 Jul 2 4 LMH 85 0 1 84
## 83 10.0 23/07/2014 Jul 2 5 LMH 24 2 0 22
## 84 10.0 23/07/2014 Jul 2 6 LMH 19 4 1 14
## 85 10.0 24/07/2014 Jul 3 7 LMH 78 1 1 76
## 86 10.0 24/07/2014 Jul 3 8 LMH 67 7 0 60
## 87 10.0 24/07/2014 Jul 3 9 LMH 32 1 1 30
## 88 10.0 22/07/2014 Jul 1 1 LMH 0 0 0 0
## 89 10.0 22/07/2014 Jul 1 2 LMH 4 3 1 0
## 90 10.0 22/07/2014 Jul 1 3 LMH 10 0 6 4
## 91 10.0 23/07/2014 Jul 2 4 MEGA 18 3 0 15
## 92 10.0 23/07/2014 Jul 2 5 MEGA 9 6 2 1
## 93 10.0 23/07/2014 Jul 2 6 MEGA 13 0 1 12
## 94 10.0 24/07/2014 Jul 3 7 MEGA 13 5 0 8
## 95 10.0 24/07/2014 Jul 3 8 MEGA 11 6 0 5
## 96 10.0 24/07/2014 Jul 3 9 MEGA 5 2 0 3
## 97 10.0 22/07/2014 Jul 1 1 MEGA 8 4 4 0
## 98 10.0 22/07/2014 Jul 1 2 MEGA 11 7 2 2
## 99 10.0 22/07/2014 Jul 1 3 MEGA 1 1 0 0
## 100 10.0 23/07/2014 Jul 2 4 MESO 15 0 0 15
## 101 10.0 23/07/2014 Jul 2 5 MESO 13 3 1 9
## 102 10.0 23/07/2014 Jul 2 6 MESO 11 5 0 6
## 103 10.0 24/07/2014 Jul 3 7 MESO 33 21 3 9
## 104 10.0 24/07/2014 Jul 3 8 MESO 14 10 1 3
## 105 10.0 24/07/2014 Jul 3 9 MESO 34 21 1 12
## 106 10.0 22/07/2014 Jul 1 1 MESO 5 4 0 1
## 107 10.0 22/07/2014 Jul 1 2 MESO 6 1 4 1
## 108 10.0 22/07/2014 Jul 1 3 MESO 7 4 1 2
## 109 13.5 11/10/2010 OctNov 2 4 CONT 6 3 3 0
## 110 13.5 11/11/2010 OctNov 2 5 CONT 4 3 0 1
## 111 13.5 11/10/2010 OctNov 2 6 CONT 6 1 5 0
## 112 13.5 30/10/2014 OctNov 3 7 CONT 3 1 1 1
## 113 13.5 30/10/2014 OctNov 3 8 CONT 2 1 1 0
## 114 13.5 31/10/2014 OctNov 3 9 CONT 3 0 2 1
## 115 13.5 13/11/2014 OctNov 1 1 CONT 9 4 4 1
## 116 13.5 13/11/2014 OctNov 1 2 CONT 6 0 5 1
## 117 13.5 14/11/2014 OctNov 1 3 CONT 5 1 3 1
## 118 13.5 11/10/2010 OctNov 2 4 LMH 20 1 0 19
## 119 13.5 11/11/2010 OctNov 2 5 LMH 7 7 0 0
## 120 13.5 11/10/2010 OctNov 2 6 LMH 12 3 6 3
## 121 13.5 30/10/2014 OctNov 3 7 LMH 35 0 8 27
## 122 13.5 31/10/2014 OctNov 3 8 LMH 8 0 7 1
## 123 13.5 31/10/2014 OctNov 3 9 LMH 9 0 6 3
## 124 13.5 13/11/2014 OctNov 1 1 LMH 14 5 9 0
## 125 13.5 13/11/2014 OctNov 1 2 LMH 9 5 3 1
## 126 13.5 14/11/2014 OctNov 1 3 LMH 8 6 2 0
## 127 13.5 11/10/2010 OctNov 2 4 MEGA 5 0 0 5
## 128 13.5 11/10/2010 OctNov 2 5 MEGA 3 0 2 1
## 129 13.5 11/10/2010 OctNov 2 6 MEGA 6 0 6 0
## 130 13.5 31/10/2014 OctNov 3 7 MEGA 5 3 2 0
## 131 13.5 31/10/2014 OctNov 3 8 MEGA 4 3 1 0
## 132 13.5 31/10/2014 OctNov 3 9 MEGA 5 1 4 0
## 133 13.5 14/11/2014 OctNov 1 1 MEGA 6 4 2 0
## 134 13.5 14/11/2014 OctNov 1 2 MEGA 3 3 0 0
## 135 13.5 14/11/2014 OctNov 1 3 MEGA 2 1 1 0
## 136 13.5 11/10/2010 OctNov 2 4 MESO 9 4 3 2
## 137 13.5 11/10/2010 OctNov 2 5 MESO 2 1 1 0
## 138 13.5 11/10/2010 OctNov 2 6 MESO 3 0 0 3
## 139 13.5 31/10/2014 OctNov 3 7 MESO 11 6 2 3
## 140 13.5 31/10/2014 OctNov 3 8 MESO 4 0 4 0
## 141 13.5 31/10/2014 OctNov 3 9 MESO 5 0 5 0
## 142 13.5 14/11/2014 OctNov 1 1 MESO 1 1 0 0
## 143 13.5 14/11/2014 OctNov 1 2 MESO 4 4 0 0
## 144 13.5 14/11/2014 OctNov 1 3 MESO 5 2 3 0
## 145 12.0 30/09/2014 Sep 2 4 CONT 2 1 0 1
## 146 12.0 30/09/2014 Sep 2 5 CONT 4 4 0 0
## 147 12.0 30/09/2014 Sep 2 6 CONT 4 3 0 1
## 148 12.0 19/09/2014 Sep 3 7 CONT 2 0 2 0
## 149 12.0 19/09/2014 Sep 3 8 CONT 1 1 0 0
## 150 12.0 19/09/2014 Sep 3 9 CONT 3 3 0 0
## 151 12.0 26/09/2014 Sep 1 1 CONT 3 2 1 0
## 152 12.0 26/09/2014 Sep 1 2 CONT 5 3 2 0
## 153 12.0 26/09/2014 Sep 1 3 CONT 9 4 5 0
## 154 12.0 30/09/2014 Sep 2 4 LMH 19 0 3 16
## 155 12.0 30/09/2014 Sep 2 5 LMH 4 3 1 0
## 156 12.0 30/09/2014 Sep 2 6 LMH 8 1 2 5
## 157 12.0 19/09/2014 Sep 3 7 LMH 15 0 3 12
## 158 12.0 19/09/2014 Sep 3 8 LMH 7 1 1 5
## 159 12.0 19/09/2014 Sep 3 9 LMH 3 1 2 0
## 160 12.0 26/09/2014 Sep 1 1 LMH 3 1 0 2
## 161 12.0 26/09/2014 Sep 1 2 LMH 2 0 1 1
## 162 12.0 26/09/2014 Sep 1 3 LMH 0 0 0 0
## 163 12.0 30/09/2014 Sep 2 4 MEGA 6 3 0 3
## 164 12.0 30/09/2014 Sep 2 5 MEGA 5 5 0 0
## 165 12.0 30/09/2014 Sep 2 6 MEGA 4 3 1 0
## 166 12.0 19/09/2014 Sep 3 7 MEGA 4 4 0 0
## 167 12.0 19/09/2014 Sep 3 8 MEGA 1 1 0 0
## 168 12.0 19/09/2014 Sep 3 9 MEGA 4 2 2 0
## 169 12.0 26/09/2014 Sep 1 1 MEGA 12 5 6 1
## 170 12.0 26/09/2014 Sep 1 2 MEGA 6 6 0 0
## 171 12.0 26/09/2014 Sep 1 3 MEGA 3 1 2 0
## 172 12.0 30/09/2014 Sep 2 4 MESO 5 3 1 1
## 173 12.0 30/09/2014 Sep 2 5 MESO 6 6 0 0
## 174 12.0 30/09/2014 Sep 2 6 MESO 4 3 1 0
## 175 12.0 19/09/2014 Sep 3 7 MESO 6 3 3 0
## 176 12.0 19/09/2014 Sep 3 8 MESO 6 2 3 1
## 177 12.0 19/09/2014 Sep 3 9 MESO 4 3 1 0
## 178 12.0 26/09/2014 Sep 1 1 MESO 11 6 5 0
## 179 12.0 26/09/2014 Sep 1 2 MESO 3 2 1 0
## 180 12.0 26/09/2014 Sep 1 3 MESO 3 2 1 0
## Rain
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## 180 640shapiro.test(tick$Total)##
## Shapiro-Wilk normality test
##
## data: tick$Total
## W = 0.64898, p-value < 2.2e-16library(car)## Loading required package: carDataleveneTest(Total ~ Treatment,
data = tick)## 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 7.506 9.346e-05 ***
## 176
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1treat_tick <- aov(Total ~ Treatment,
data = tick)
summary(treat_tick)## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 3 3387 1128.9 8.67 2.13e-05 ***
## Residuals 176 22915 130.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(treat_tick)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Total ~ Treatment, data = tick)
##
## $Treatment
## diff lwr upr p adj
## LMH-CONT 11.844444 5.6050266 18.083862 0.0000115
## MEGA-CONT 3.177778 -3.0616401 9.417196 0.5508095
## MESO-CONT 5.355556 -0.8838623 11.594973 0.1201486
## MEGA-LMH -8.666667 -14.9060845 -2.427249 0.0022968
## MESO-LMH -6.488889 -12.7283067 -0.249471 0.0380749
## MESO-MEGA 2.177778 -4.0616401 8.417196 0.8020299boxplot(Total ~ Treatment,
data = tick)
PART 1b: violations of assumptions. Answer the questions below:
Code: choose a transformation method of your choice and apply it to the “total” column
Code: re-test the assumptions from Part 1a Q4
Question: are the assumptions met?
- The p-value for the Shapiro test is 0.03674 so the assumption of normality is still not met. The p-value for the levene test is 0.578 therefore the assumption of homogeneity of varience is met.
Code: Run the alternative non-parametric test appropriate for this data
Question: what does the output of 4 tell us about ticks and treatment?
- The output of 4 is a p-value of 2.356e-05 which tells us to reject the null hypothesis. Therefore, at least one of the treatment groups has a median different from the rest, indicating that different treatment groups have an effect on total ticks.
tick$log_Total <- log(tick$Total)
tick## Period Date Month Level Replicate Treatment Total RHPU RHPR RHPV
## 1 11.0 20/08/2014 Aug 2 4 CONT 1 0 0 1
## 2 11.0 20/08/2014 Aug 2 5 CONT 1 0 0 1
## 3 11.0 20/08/2014 Aug 2 6 CONT 1 0 0 1
## 4 11.0 21/08/2014 Aug 3 7 CONT 0 0 0 0
## 5 11.0 21/08/2014 Aug 3 8 CONT 0 0 0 0
## 6 11.0 21/08/2014 Aug 3 9 CONT 5 4 0 1
## 7 11.0 22/08/2014 Aug 1 1 CONT 4 3 0 1
## 8 11.0 22/08/2014 Aug 1 2 CONT 0 0 0 0
## 9 11.0 22/08/2014 Aug 1 3 CONT 1 0 0 1
## 10 11.0 20/08/2014 Aug 2 4 LMH 37 0 1 36
## 11 11.0 20/08/2014 Aug 2 5 LMH 5 0 1 4
## 12 11.0 20/08/2014 Aug 2 6 LMH 13 1 1 11
## 13 11.0 21/08/2014 Aug 3 7 LMH 45 0 2 43
## 14 11.0 21/08/2014 Aug 3 8 LMH 10 3 0 7
## 15 11.0 21/08/2014 Aug 3 9 LMH 9 2 0 7
## 16 11.0 22/08/2014 Aug 1 1 LMH 4 0 3 1
## 17 11.0 22/08/2014 Aug 1 2 LMH 5 1 4 0
## 18 11.0 22/08/2014 Aug 1 3 LMH 0 0 0 0
## 19 11.0 20/08/2014 Aug 2 4 MEGA 17 3 1 13
## 20 11.0 20/08/2014 Aug 2 5 MEGA 7 3 0 4
## 21 11.0 20/08/2014 Aug 2 6 MEGA 6 1 0 5
## 22 11.0 21/08/2014 Aug 3 7 MEGA 2 0 0 2
## 23 11.0 21/08/2014 Aug 3 8 MEGA 1 0 0 1
## 24 11.0 21/08/2014 Aug 3 9 MEGA 1 0 0 1
## 25 11.0 22/08/2014 Aug 1 1 MEGA 5 1 2 2
## 26 11.0 22/08/2014 Aug 1 2 MEGA 2 1 0 1
## 27 11.0 22/08/2014 Aug 1 3 MEGA 6 6 0 0
## 28 11.0 20/08/2014 Aug 2 4 MESO 28 5 0 23
## 29 11.0 20/08/2014 Aug 2 5 MESO 14 12 1 1
## 30 11.0 20/08/2014 Aug 2 6 MESO 8 8 0 0
## 31 11.0 21/08/2014 Aug 3 7 MESO 8 5 0 3
## 32 11.0 21/08/2014 Aug 3 8 MESO 15 13 0 2
## 33 11.0 21/08/2014 Aug 3 9 MESO 10 7 0 3
## 34 11.0 22/08/2014 Aug 1 1 MESO 4 0 1 3
## 35 11.0 22/08/2014 Aug 1 2 MESO 2 2 0 0
## 36 11.0 22/08/2014 Aug 1 3 MESO 0 0 0 0
## 37 4.0 27/01/2014 Jan 2 4 CONT 1 0 0 1
## 38 4.0 17/01/2014 Jan 2 5 CONT 14 11 3 0
## 39 4.0 27/01/2014 Jan 2 6 CONT 26 0 21 5
## 40 4.0 20/01/2014 Jan 3 7 CONT 8 6 0 2
## 41 4.0 25/01/2014 Jan 3 8 CONT 4 3 0 1
## 42 4.0 20/01/2014 Jan 3 9 CONT 5 5 0 0
## 43 4.0 14/01/2014 Jan 1 1 CONT 14 3 11 0
## 44 4.0 14/01/2014 Jan 1 2 CONT 4 1 3 0
## 45 4.0 18/01/2014 Jan 1 3 CONT 2 2 0 0
## 46 4.0 27/01/2014 Jan 2 4 LMH 4 0 0 4
## 47 4.0 17/01/2014 Jan 2 5 LMH 2 2 0 0
## 48 4.0 27/01/2014 Jan 2 6 LMH 8 4 0 4
## 49 4.0 20/01/2014 Jan 3 7 LMH 8 0 0 8
## 50 4.0 25/01/2014 Jan 3 8 LMH 3 0 0 3
## 51 4.0 20/01/2014 Jan 3 9 LMH 19 1 0 18
## 52 4.0 14/01/2014 Jan 1 1 LMH 38 0 38 0
## 53 4.0 14/01/2014 Jan 1 2 LMH 25 0 25 0
## 54 4.0 18/01/2014 Jan 1 3 LMH 0 0 0 0
## 55 4.0 27/01/2014 Jan 2 4 MEGA 16 3 12 1
## 56 4.0 17/01/2014 Jan 2 5 MEGA 9 6 3 0
## 57 4.0 27/01/2014 Jan 2 6 MEGA 0 0 0 0
## 58 4.0 20/01/2014 Jan 3 7 MEGA 11 2 7 2
## 59 4.0 25/01/2014 Jan 3 8 MEGA 19 13 2 4
## 60 4.0 20/01/2014 Jan 3 9 MEGA 21 13 3 5
## 61 4.0 14/01/2014 Jan 1 1 MEGA 21 0 21 0
## 62 4.0 14/01/2014 Jan 1 2 MEGA 14 3 11 0
## 63 4.0 18/01/2014 Jan 1 3 MEGA 16 2 14 0
## 64 4.0 27/01/2014 Jan 2 4 MESO 17 0 17 0
## 65 4.0 17/01/2014 Jan 2 5 MESO 10 3 6 1
## 66 4.0 27/01/2014 Jan 2 6 MESO 9 5 3 1
## 67 4.0 20/01/2014 Jan 3 7 MESO 34 8 25 1
## 68 4.0 25/01/2014 Jan 3 8 MESO 13 6 6 1
## 69 4.0 20/01/2014 Jan 3 9 MESO 19 7 5 7
## 70 4.0 14/01/2014 Jan 1 1 MESO 16 0 16 0
## 71 4.0 14/01/2014 Jan 1 2 MESO 3 0 3 0
## 72 4.0 18/01/2014 Jan 1 3 MESO 5 1 4 0
## 73 10.0 23/07/2014 Jul 2 4 CONT 7 0 1 6
## 74 10.0 23/07/2014 Jul 2 5 CONT 11 11 0 0
## 75 10.0 23/07/2014 Jul 2 6 CONT 2 0 0 2
## 76 10.0 24/07/2014 Jul 3 7 CONT 10 0 0 10
## 77 10.0 24/07/2014 Jul 3 8 CONT 0 0 0 0
## 78 10.0 24/07/2014 Jul 3 9 CONT 2 0 0 2
## 79 10.0 22/07/2014 Jul 1 1 CONT 4 0 0 4
## 80 10.0 22/07/2014 Jul 1 2 CONT 0 0 0 0
## 81 10.0 22/07/2014 Jul 1 3 CONT 0 0 0 0
## 82 10.0 23/07/2014 Jul 2 4 LMH 85 0 1 84
## 83 10.0 23/07/2014 Jul 2 5 LMH 24 2 0 22
## 84 10.0 23/07/2014 Jul 2 6 LMH 19 4 1 14
## 85 10.0 24/07/2014 Jul 3 7 LMH 78 1 1 76
## 86 10.0 24/07/2014 Jul 3 8 LMH 67 7 0 60
## 87 10.0 24/07/2014 Jul 3 9 LMH 32 1 1 30
## 88 10.0 22/07/2014 Jul 1 1 LMH 0 0 0 0
## 89 10.0 22/07/2014 Jul 1 2 LMH 4 3 1 0
## 90 10.0 22/07/2014 Jul 1 3 LMH 10 0 6 4
## 91 10.0 23/07/2014 Jul 2 4 MEGA 18 3 0 15
## 92 10.0 23/07/2014 Jul 2 5 MEGA 9 6 2 1
## 93 10.0 23/07/2014 Jul 2 6 MEGA 13 0 1 12
## 94 10.0 24/07/2014 Jul 3 7 MEGA 13 5 0 8
## 95 10.0 24/07/2014 Jul 3 8 MEGA 11 6 0 5
## 96 10.0 24/07/2014 Jul 3 9 MEGA 5 2 0 3
## 97 10.0 22/07/2014 Jul 1 1 MEGA 8 4 4 0
## 98 10.0 22/07/2014 Jul 1 2 MEGA 11 7 2 2
## 99 10.0 22/07/2014 Jul 1 3 MEGA 1 1 0 0
## 100 10.0 23/07/2014 Jul 2 4 MESO 15 0 0 15
## 101 10.0 23/07/2014 Jul 2 5 MESO 13 3 1 9
## 102 10.0 23/07/2014 Jul 2 6 MESO 11 5 0 6
## 103 10.0 24/07/2014 Jul 3 7 MESO 33 21 3 9
## 104 10.0 24/07/2014 Jul 3 8 MESO 14 10 1 3
## 105 10.0 24/07/2014 Jul 3 9 MESO 34 21 1 12
## 106 10.0 22/07/2014 Jul 1 1 MESO 5 4 0 1
## 107 10.0 22/07/2014 Jul 1 2 MESO 6 1 4 1
## 108 10.0 22/07/2014 Jul 1 3 MESO 7 4 1 2
## 109 13.5 11/10/2010 OctNov 2 4 CONT 6 3 3 0
## 110 13.5 11/11/2010 OctNov 2 5 CONT 4 3 0 1
## 111 13.5 11/10/2010 OctNov 2 6 CONT 6 1 5 0
## 112 13.5 30/10/2014 OctNov 3 7 CONT 3 1 1 1
## 113 13.5 30/10/2014 OctNov 3 8 CONT 2 1 1 0
## 114 13.5 31/10/2014 OctNov 3 9 CONT 3 0 2 1
## 115 13.5 13/11/2014 OctNov 1 1 CONT 9 4 4 1
## 116 13.5 13/11/2014 OctNov 1 2 CONT 6 0 5 1
## 117 13.5 14/11/2014 OctNov 1 3 CONT 5 1 3 1
## 118 13.5 11/10/2010 OctNov 2 4 LMH 20 1 0 19
## 119 13.5 11/11/2010 OctNov 2 5 LMH 7 7 0 0
## 120 13.5 11/10/2010 OctNov 2 6 LMH 12 3 6 3
## 121 13.5 30/10/2014 OctNov 3 7 LMH 35 0 8 27
## 122 13.5 31/10/2014 OctNov 3 8 LMH 8 0 7 1
## 123 13.5 31/10/2014 OctNov 3 9 LMH 9 0 6 3
## 124 13.5 13/11/2014 OctNov 1 1 LMH 14 5 9 0
## 125 13.5 13/11/2014 OctNov 1 2 LMH 9 5 3 1
## 126 13.5 14/11/2014 OctNov 1 3 LMH 8 6 2 0
## 127 13.5 11/10/2010 OctNov 2 4 MEGA 5 0 0 5
## 128 13.5 11/10/2010 OctNov 2 5 MEGA 3 0 2 1
## 129 13.5 11/10/2010 OctNov 2 6 MEGA 6 0 6 0
## 130 13.5 31/10/2014 OctNov 3 7 MEGA 5 3 2 0
## 131 13.5 31/10/2014 OctNov 3 8 MEGA 4 3 1 0
## 132 13.5 31/10/2014 OctNov 3 9 MEGA 5 1 4 0
## 133 13.5 14/11/2014 OctNov 1 1 MEGA 6 4 2 0
## 134 13.5 14/11/2014 OctNov 1 2 MEGA 3 3 0 0
## 135 13.5 14/11/2014 OctNov 1 3 MEGA 2 1 1 0
## 136 13.5 11/10/2010 OctNov 2 4 MESO 9 4 3 2
## 137 13.5 11/10/2010 OctNov 2 5 MESO 2 1 1 0
## 138 13.5 11/10/2010 OctNov 2 6 MESO 3 0 0 3
## 139 13.5 31/10/2014 OctNov 3 7 MESO 11 6 2 3
## 140 13.5 31/10/2014 OctNov 3 8 MESO 4 0 4 0
## 141 13.5 31/10/2014 OctNov 3 9 MESO 5 0 5 0
## 142 13.5 14/11/2014 OctNov 1 1 MESO 1 1 0 0
## 143 13.5 14/11/2014 OctNov 1 2 MESO 4 4 0 0
## 144 13.5 14/11/2014 OctNov 1 3 MESO 5 2 3 0
## 145 12.0 30/09/2014 Sep 2 4 CONT 2 1 0 1
## 146 12.0 30/09/2014 Sep 2 5 CONT 4 4 0 0
## 147 12.0 30/09/2014 Sep 2 6 CONT 4 3 0 1
## 148 12.0 19/09/2014 Sep 3 7 CONT 2 0 2 0
## 149 12.0 19/09/2014 Sep 3 8 CONT 1 1 0 0
## 150 12.0 19/09/2014 Sep 3 9 CONT 3 3 0 0
## 151 12.0 26/09/2014 Sep 1 1 CONT 3 2 1 0
## 152 12.0 26/09/2014 Sep 1 2 CONT 5 3 2 0
## 153 12.0 26/09/2014 Sep 1 3 CONT 9 4 5 0
## 154 12.0 30/09/2014 Sep 2 4 LMH 19 0 3 16
## 155 12.0 30/09/2014 Sep 2 5 LMH 4 3 1 0
## 156 12.0 30/09/2014 Sep 2 6 LMH 8 1 2 5
## 157 12.0 19/09/2014 Sep 3 7 LMH 15 0 3 12
## 158 12.0 19/09/2014 Sep 3 8 LMH 7 1 1 5
## 159 12.0 19/09/2014 Sep 3 9 LMH 3 1 2 0
## 160 12.0 26/09/2014 Sep 1 1 LMH 3 1 0 2
## 161 12.0 26/09/2014 Sep 1 2 LMH 2 0 1 1
## 162 12.0 26/09/2014 Sep 1 3 LMH 0 0 0 0
## 163 12.0 30/09/2014 Sep 2 4 MEGA 6 3 0 3
## 164 12.0 30/09/2014 Sep 2 5 MEGA 5 5 0 0
## 165 12.0 30/09/2014 Sep 2 6 MEGA 4 3 1 0
## 166 12.0 19/09/2014 Sep 3 7 MEGA 4 4 0 0
## 167 12.0 19/09/2014 Sep 3 8 MEGA 1 1 0 0
## 168 12.0 19/09/2014 Sep 3 9 MEGA 4 2 2 0
## 169 12.0 26/09/2014 Sep 1 1 MEGA 12 5 6 1
## 170 12.0 26/09/2014 Sep 1 2 MEGA 6 6 0 0
## 171 12.0 26/09/2014 Sep 1 3 MEGA 3 1 2 0
## 172 12.0 30/09/2014 Sep 2 4 MESO 5 3 1 1
## 173 12.0 30/09/2014 Sep 2 5 MESO 6 6 0 0
## 174 12.0 30/09/2014 Sep 2 6 MESO 4 3 1 0
## 175 12.0 19/09/2014 Sep 3 7 MESO 6 3 3 0
## 176 12.0 19/09/2014 Sep 3 8 MESO 6 2 3 1
## 177 12.0 19/09/2014 Sep 3 9 MESO 4 3 1 0
## 178 12.0 26/09/2014 Sep 1 1 MESO 11 6 5 0
## 179 12.0 26/09/2014 Sep 1 2 MESO 3 2 1 0
## 180 12.0 26/09/2014 Sep 1 3 MESO 3 2 1 0
## Rain log_Total
## 1 580 0.0000000
## 2 580 0.0000000
## 3 580 0.0000000
## 4 440 -Inf
## 5 440 -Inf
## 6 440 1.6094379
## 7 640 1.3862944
## 8 640 -Inf
## 9 640 0.0000000
## 10 580 3.6109179
## 11 580 1.6094379
## 12 580 2.5649494
## 13 440 3.8066625
## 14 440 2.3025851
## 15 440 2.1972246
## 16 640 1.3862944
## 17 640 1.6094379
## 18 640 -Inf
## 19 580 2.8332133
## 20 580 1.9459101
## 21 580 1.7917595
## 22 440 0.6931472
## 23 440 0.0000000
## 24 440 0.0000000
## 25 640 1.6094379
## 26 640 0.6931472
## 27 640 1.7917595
## 28 580 3.3322045
## 29 580 2.6390573
## 30 580 2.0794415
## 31 440 2.0794415
## 32 440 2.7080502
## 33 440 2.3025851
## 34 640 1.3862944
## 35 640 0.6931472
## 36 640 -Inf
## 37 580 0.0000000
## 38 580 2.6390573
## 39 580 3.2580965
## 40 440 2.0794415
## 41 440 1.3862944
## 42 440 1.6094379
## 43 640 2.6390573
## 44 640 1.3862944
## 45 640 0.6931472
## 46 580 1.3862944
## 47 580 0.6931472
## 48 580 2.0794415
## 49 440 2.0794415
## 50 440 1.0986123
## 51 440 2.9444390
## 52 640 3.6375862
## 53 640 3.2188758
## 54 640 -Inf
## 55 580 2.7725887
## 56 580 2.1972246
## 57 580 -Inf
## 58 440 2.3978953
## 59 440 2.9444390
## 60 440 3.0445224
## 61 640 3.0445224
## 62 640 2.6390573
## 63 640 2.7725887
## 64 580 2.8332133
## 65 580 2.3025851
## 66 580 2.1972246
## 67 440 3.5263605
## 68 440 2.5649494
## 69 440 2.9444390
## 70 640 2.7725887
## 71 640 1.0986123
## 72 640 1.6094379
## 73 580 1.9459101
## 74 580 2.3978953
## 75 580 0.6931472
## 76 440 2.3025851
## 77 440 -Inf
## 78 440 0.6931472
## 79 640 1.3862944
## 80 640 -Inf
## 81 640 -Inf
## 82 580 4.4426513
## 83 580 3.1780538
## 84 580 2.9444390
## 85 440 4.3567088
## 86 440 4.2046926
## 87 440 3.4657359
## 88 640 -Inf
## 89 640 1.3862944
## 90 640 2.3025851
## 91 580 2.8903718
## 92 580 2.1972246
## 93 580 2.5649494
## 94 440 2.5649494
## 95 440 2.3978953
## 96 440 1.6094379
## 97 640 2.0794415
## 98 640 2.3978953
## 99 640 0.0000000
## 100 580 2.7080502
## 101 580 2.5649494
## 102 580 2.3978953
## 103 440 3.4965076
## 104 440 2.6390573
## 105 440 3.5263605
## 106 640 1.6094379
## 107 640 1.7917595
## 108 640 1.9459101
## 109 580 1.7917595
## 110 580 1.3862944
## 111 580 1.7917595
## 112 440 1.0986123
## 113 440 0.6931472
## 114 440 1.0986123
## 115 640 2.1972246
## 116 640 1.7917595
## 117 640 1.6094379
## 118 580 2.9957323
## 119 580 1.9459101
## 120 580 2.4849066
## 121 440 3.5553481
## 122 440 2.0794415
## 123 440 2.1972246
## 124 640 2.6390573
## 125 640 2.1972246
## 126 640 2.0794415
## 127 580 1.6094379
## 128 580 1.0986123
## 129 580 1.7917595
## 130 440 1.6094379
## 131 440 1.3862944
## 132 440 1.6094379
## 133 640 1.7917595
## 134 640 1.0986123
## 135 640 0.6931472
## 136 580 2.1972246
## 137 580 0.6931472
## 138 580 1.0986123
## 139 440 2.3978953
## 140 440 1.3862944
## 141 440 1.6094379
## 142 640 0.0000000
## 143 640 1.3862944
## 144 640 1.6094379
## 145 580 0.6931472
## 146 580 1.3862944
## 147 580 1.3862944
## 148 440 0.6931472
## 149 440 0.0000000
## 150 440 1.0986123
## 151 640 1.0986123
## 152 640 1.6094379
## 153 640 2.1972246
## 154 580 2.9444390
## 155 580 1.3862944
## 156 580 2.0794415
## 157 440 2.7080502
## 158 440 1.9459101
## 159 440 1.0986123
## 160 640 1.0986123
## 161 640 0.6931472
## 162 640 -Inf
## 163 580 1.7917595
## 164 580 1.6094379
## 165 580 1.3862944
## 166 440 1.3862944
## 167 440 0.0000000
## 168 440 1.3862944
## 169 640 2.4849066
## 170 640 1.7917595
## 171 640 1.0986123
## 172 580 1.6094379
## 173 580 1.7917595
## 174 580 1.3862944
## 175 440 1.7917595
## 176 440 1.7917595
## 177 440 1.3862944
## 178 640 2.3978953
## 179 640 1.0986123
## 180 640 1.0986123tick[is.na(tick) | tick == "-Inf"] <- NA
shapiro.test(tick$log_Total)##
## Shapiro-Wilk normality test
##
## data: tick$log_Total
## W = 0.98291, p-value = 0.03674library(car)
log_Levene <- leveneTest(log_Total ~ Treatment, data = tick)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.log_Levene## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 0.6596 0.578
## 164kruskal.test(log_Total ~ Treatment, data = tick)##
## Kruskal-Wallis rank sum test
##
## data: log_Total by Treatment
## Kruskal-Wallis chi-squared = 24.121, df = 3, p-value = 2.356e-05PART 2: does environment influence ticks?
- Question: what type of test would be appropriate to run in order to compare total ticks by rain? Why?
- A regression test would be best because there are two numerical variables and rain is the pre-determined explanatory variable and the total ticks are the response variable.
Code: run the test
Question: using the output from 2, tell me what the model says about ticks and rainfall. Include in your answer an interpretation of the p-value and an interpretation of the R^2 value
- The p=value is = 0.01957 telling us the the rain variable is statistically significant. The adjusted R^2 value is 0.02479 telling us that rain fall explains some of the variance in total ticks.
Code: create a visual that shows the relationship between rainfall and ticks
Code: add a best fit line to help visualize the pattern
Question: Do ticks prefer wet or dry climates?
- Ticks slightly prefer dry climates.
- Code: predict the number of ticks observed in a region that experiences 500 mm/year mean rainfall
tick.lm <- lm(formula = Total ~ Rain, data = tick)
summary(tick.lm)##
## Call:
## lm(formula = Total ~ Rain, data = tick)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.471 -6.593 -2.959 2.163 76.041
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 23.50781 5.95874 3.945 0.000115 ***
## Rain -0.02508 0.01065 -2.356 0.019565 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.97 on 178 degrees of freedom
## Multiple R-squared: 0.03024, Adjusted R-squared: 0.02479
## F-statistic: 5.55 on 1 and 178 DF, p-value: 0.01957plot(x = tick$Rain, y = tick$Total)
abline(a = coef(tick.lm)[1], b = coef(tick.lm)[2], col = "red") 
predict(tick.lm, list(Rain = 500))## 1
## 10.96561