Publications by Dawson
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##Q1: Revisit conclusion of T-test lab: apply corrections for 2015 data set comparisons of tree heights. ##Conclusion Corrections Applied: divide the critical numbber by each trial/test: p = 0.05/3 = 0.01666666667 ##Comparing A, B, C 2015:Hypothesis, Result Statistics, Conclusion All the conclusions are equal, H0 is true. The p-value is 3 times l...
8176 sym R (7423 sym/37 pcs) 5 img
LAB 9 –Analysis of variance and interactions
library(ggplot2) RADISH <- read.csv("~/Desktop/BIO STATS/LAB 9 - Interactions/RADISH.csv") ##Q1: Using what you learned in today’s lecture, complete a full statistical analysis* of for shoot length (i.e., GREEN)? GREEN (SHOOT LENGTH)= WATER + SOIL + WATER:SOIL HYPOTHESIS: H0 Interaction= there is no interactions between the effect of water and...
3383 sym R (1765 sym/8 pcs) 2 img
Lab 10 Interactions
sheep <- read.csv("~/Desktop/LO;∏÷ÒØ9=]\\[/Lab 10/sheep.csv") library(ggplot2) ##Q1 What conclusion do you think Dr. Sharp is drawing from this analysis? Dr. Sharp would draw that the sheep sex impacts the number of lookups. ##Q2 In its current form, this study has a major flaw. Identify the flaw and what clue(s) from the anova table enab...
2363 sym R (436 sym/3 pcs) 1 img
Lab 11
##Q1Before you start looking at the model assumptions, why should you expect that your data set might require a transformation? Hint: You can find the answer towards the end of the lecture. The data would need a tranformation by amount of radiation, and block number. ##Q2Using R, conduct a complete statistical analysis for this experiment without...
2999 sym R (3491 sym/13 pcs) 5 img
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library(ggplot2) Clover <- read.csv("~/Desktop/Plant Lab/FUN - CC.csv") MungBean <- read.csv("~/Desktop/Plant Lab/FUN - MB.csv") woInter <- read.csv("~/Desktop/FUN - WO INTER.csv") Mung Bean Word Equation: MUNG BEAN AVERAGE MASS = MUNG BEAN SEEDS + PERCENT GERMINATION + GERMINATION:SEEDS Mung Bean Hypotheses including all data sets H0i: Ther...
3349 sym R (3758 sym/9 pcs)
Algae Competition Lab
library(ggplot2) algae<- read.csv("~/Desktop/Lab 4_ Algae - %%.csv") DAY 8 TURPIDITY = SINGLE TREATMENTS modelSINGLEDAY8 <- lm(algae$Day.8~factor(algae$Sc)+factor(algae$Co)+factor(algae$Na)+factor(algae$Ch)+factor(algae$Cl)+factor(algae$Se)) anova(modelSINGLEDAY8) Conclusion: There is an effect of algae Cl on day 8 turpidity (F(3,18) = 3.4047; P...
3211 sym R (1111 sym/5 pcs)