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To perform a Wilcoxon Signed Ranks Test using R, one can use wilcox.test() function. Data, R code and its output is given below. Data # Data in two numeric vectors # +++++++ # Weight of the mice before treatment before <-c(200.1, 190.9, 192.7, 213, 241.4, 196.9, 172.2, 185.5, 205.2, 193.7) * Weight of the mice after treatment after <-c(392.9, 393.2, 345.1, 393, 434, 427.9, 422, 383.9, 392.3, 352.2) # Create a data frame my_data <- data.frame( +++++++ group = rep(c ("before", "after"), each = 10), weight = c(before, after) R code res <- wilcox.test (before, after, paired = TRUE)

To perform a Wilcoxon Signed Ranks Test using R, one can use wilcox.test() function. Data, R code and its output is given below. Data # Data in two numeric vectors # +++++++ # Weight of the mice before treatment before <-c(200.1, 190.9, 192.7, 213, 241.4, 196.9, 172.2, 185.5, 205.2, 193.7) * Weight of the mice after treatment after <-c(392.9, 393.2, 345.1, 393, 434, 427.9, 422, 383.9, 392.3, 352.2) # Create a data frame my_data <- data.frame( +++++++ group = rep(c ("before", "after"), each = 10), weight = c(before, after) R code res <- wilcox.test (before, after, paired = TRUE)

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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To perform a Wilcoxon Signed Ranks Test using R, one can use wilcox.test () function. Data, R code and its output is given below. Data # Data in two numeric vectors # ++++++++++++++++++++++++++ # Weight of the mice before treatment before <-c(200.1, 190.9, 192.7, 213, 241.4, 196.9, 172.2, 185.5, 205.2, 193.7) # weight of the mice after treatment after <-c(392.9, 393.2, 345.1, 393, 434, 427.9, 422, 383.9, 392.3, 352.2) # create a data frame my_data <- data.frame ( group = rep (c ("before", "after"), each = weight = c (before, after) 10), R code res <- wilcox.test (before, after, paired = TRUE) res R output wilcoxon signed rank test data: before and after v = 0, p-value = 0.001953 alternative hypothesis: true location shift is not equal to 0 Note: The dataset is about the weight of 10 mice before and after the treatment. Which of the following is the correct interpretation of the data analysis using R? I. We can conclude that the mean weight of the mice before treatment is significantly different from the mean weight after treatment with a p-value = 0.001953. II. We can conclude that the mean weight of the mice before treatment is the same with the mean weight after treatment with a p-value = 0.001953. III. We can conclude that the median weight of the mice before treatment is the same with the median weight after treatment with a p-value = 0.001953. IV. We can conclude that the median weight of the mice before treatment is significantly different from the median weight after treatment with a p-value = 0.001953. a. I only ь. П оnly с. Ш оnly d. IV only В
Transcribed Image Text:To perform a Wilcoxon Signed Ranks Test using R, one can use wilcox.test () function. Data, R code and its output is given below. Data # Data in two numeric vectors # ++++++++++++++++++++++++++ # Weight of the mice before treatment before <-c(200.1, 190.9, 192.7, 213, 241.4, 196.9, 172.2, 185.5, 205.2, 193.7) # weight of the mice after treatment after <-c(392.9, 393.2, 345.1, 393, 434, 427.9, 422, 383.9, 392.3, 352.2) # create a data frame my_data <- data.frame ( group = rep (c ("before", "after"), each = weight = c (before, after) 10), R code res <- wilcox.test (before, after, paired = TRUE) res R output wilcoxon signed rank test data: before and after v = 0, p-value = 0.001953 alternative hypothesis: true location shift is not equal to 0 Note: The dataset is about the weight of 10 mice before and after the treatment. Which of the following is the correct interpretation of the data analysis using R? I. We can conclude that the mean weight of the mice before treatment is significantly different from the mean weight after treatment with a p-value = 0.001953. II. We can conclude that the mean weight of the mice before treatment is the same with the mean weight after treatment with a p-value = 0.001953. III. We can conclude that the median weight of the mice before treatment is the same with the median weight after treatment with a p-value = 0.001953. IV. We can conclude that the median weight of the mice before treatment is significantly different from the median weight after treatment with a p-value = 0.001953. a. I only ь. П оnly с. Ш оnly d. IV only В
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