How to solve this problem for my pre calculous class.
1 answer:
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Answer:
The correct answer is:
Poisson (A.)
Step-by-step explanation:
A Poisson distribution is used to model the number of events occurring within a given time interval, when the average number of times that the event occurs within the time interval is given.
Lambda ( λ ) is a rate parameter in Poisson's distribution, and it is used to represent "event/time", and it simply represents <em>the expected number of events in the interval.</em>
Answer:
a) For this case we can use the following R code to construct the qq plot
> data<-c(28.8, 24.4, 30.1, 25.6, 26.4, 23.9, 22.1, 22.5, 27.6, 28.1, 20.8, 27.7, 24.4, 25.1, 24.6, 26.3, 28.2, 22.2, 26.3, 24.4)
# The above line is in order to store the data in a vector
> qqnorm(data, pch = 1, frame = FALSE)
# The line above is in order to calculate the quantiles from the data assumin Normal distribution
> qqline(data, col = "steelblue", lwd = 2)
# The line above is in order to put a line for the theoretical dsitribution
The result is on the figure attached.
b) For this case as we can see on the figure attached the calculated quantiles are not far from the theorical quantiles given byt the straaigth blue line so then we can conclude that the distribution seems to be normal.
Step-by-step explanation:
For this case we have the following data:
28.8, 24.4, 30.1, 25.6, 26.4, 23.9, 22.1, 22.5, 27.6, 28.1, 20.8, 27.7, 24.4, 25.1, 24.6, 26.3, 28.2, 22.2, 26.3, 24.4
The quantile-quantile or q-q plot is a graphical procedure in order to check the validity of a distributional assumption for a data set. We just need to calculate "the theoretically expected value for each data point based on the distribution in question".
If the values are asusted to the assumed distribution, we will see that "the points on the q-q plot will fall approximately on a straight line"
For this case our distribution assumed is normal.
Part a
For this case we can use the following R code to construct the qq plot
> data<-c(28.8, 24.4, 30.1, 25.6, 26.4, 23.9, 22.1, 22.5, 27.6, 28.1, 20.8, 27.7, 24.4, 25.1, 24.6, 26.3, 28.2, 22.2, 26.3, 24.4)
# The above line is in order to store the data in a vector
> qqnorm(data, pch = 1, frame = FALSE)
# The line above is in order to calculate the quantiles from the data assuming Normal distribution (0,1)
> qqline(data, col = "steelblue", lwd = 2)
# The line above is in order to put a line for the theoretical distribution
The result is on the figure attached.
Part b
For this case as we can see on the figure attached the calculated quantiles are not far from the theorical quantiles given byt the straaigth blue line so then we can conclude that the distribution seems to be normal.
