Is there any significant difference in the mean weights after treatment?
Transcribed Image Text:#Paired t-test
+++++++++#
+++++++++++++
#Compute the difference between the two groups before and after
"after"] - my_data$weight [my_data$group == "before"]
my_data$diff <- my_data$weight [my_data$group
> # Compute the mean and the standard deviation of the difference
> mean(my_data$diff)
[1] 194.49
> sd(my_data$diff)
[1] 28.6656
#Paired t-test
> t.test(my_data$diff, mu = 0, alternative = "greater")
One Sample t-test
data: my_data$diff
t = 30.342, df = 19, p-value < 2.2e-16
==
sample estimates:
mean of x
194.49
alternative hypothesis: true mean is greater than 0
95 percent confidence interval:
183.4066
Inf
Definition Definition Measure of central tendency that is the average of a given data set. The mean value is evaluated as the quotient of the sum of all observations by the sample size. The mean, in contrast to a median, is affected by extreme values. Very large or very small values can distract the mean from the center of the data. Arithmetic mean: The most common type of mean is the arithmetic mean. It is evaluated using the formula: μ = 1 N ∑ i = 1 N x i Other types of means are the geometric mean, logarithmic mean, and harmonic mean. Geometric mean: The nth root of the product of n observations from a data set is defined as the geometric mean of the set: G = x 1 x 2 ... x n n Logarithmic mean: The difference of the natural logarithms of the two numbers, divided by the difference between the numbers is the logarithmic mean of the two numbers. The logarithmic mean is used particularly in heat transfer and mass transfer. ln x 2 − ln x 1 x 2 − x 1 Harmonic mean: The inverse of the arithmetic mean of the inverses of all the numbers in a data set is the harmonic mean of the data. 1 1 x 1 + 1 x 2 + ...
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![#Paired t-test
+++++++++#
+++++++++++++
#Compute the difference between the two groups before and after
"after"] - my_data$weight [my_data$group == "before"]
my_data$diff <- my_data$weight [my_data$group
> # Compute the mean and the standard deviation of the difference
> mean(my_data$diff)
[1] 194.49
> sd(my_data$diff)
[1] 28.6656
#Paired t-test
> t.test(my_data$diff, mu = 0, alternative = "greater")
One Sample t-test
data: my_data$diff
t = 30.342, df = 19, p-value < 2.2e-16
==
sample estimates:
mean of x
194.49
alternative hypothesis: true mean is greater than 0
95 percent confidence interval:
183.4066
Inf](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F6eaf2ee0-ddb5-4c4e-981a-5e79be6da71c%2Fdec7e74c-87ef-44de-bf83-7234b8085f34%2Fv7dzq2_processed.png&w=3840&q=75)
