Descriptive Statistics Project
[Name of the Writer]
[Name of the Institution]
Descriptive Statistics Project
Find the appropriate measure of center. Discuss why the chosen measure is most appropriate. Why did you decide against other possible measures of center?
In this assignment, I choose the values of Departure Delay for further calculations and applying the statistical formulas for analysis. The appropriate measure of center for this set of data is mean. Mean is the most common and widely used method of finding the measure of center. The mean is usually affected by extreme values. If these values are higher than the rest of the data, it could not give correct information. Here we have a normal range will relatively better extreme numbers. The lowest number is -24, and the highest number is 23. The total number of population is 500. When the range of the data is not too higher, we can use the mean to find the center of the measure. It is relatively easy and gives proper information of the average numbers.
Find the appropriate measure of variation. The measure of variation chosen here should match the measure of center chosen in Part 1.
In the variation, we find the difference in the occurrence of data. I used simply the maximum and minimum value of the sample size. The maximum value is 23, and the minimum value is -24.
Find the graph(s) needed to describe the data appropriately. These may be done by hand and inserted into the Word document.
Define a random variable (X) so that your chosen data set represents values of X.
Random variable X is the variable which has some possible outcomes in numerical terms of a random phenomenon. Random variables are of two types known as the discrete and random variables(Shirazi et al., 2016).
Is your chosen random variable discrete or continuous? Explain how you know.
The chosen data set is the discrete random variable because it has no infinite number. All the numbers are countable which are numerical and could be count easily.
Would the Normal or Binomial distribution be a good fit for the underlying sample distribution of X? If one of them is a good fit, state how you would approximate the distribution parameters.
The binomial distribution is quite a different phenomenon from that of the normal distribution. Binomial distribution always has two outcomes such as discrete and continuous. This data is normal because it has many outcomes; a plane could reach in time, early and late.
Calculate the probability that a flight will depart early or on-time.
The probability of a flight that will depart early is 65 percent, and the probability of on-time depart is 7 percent.
Calculate the probability that a flight will arrive late.
The probability of the flight to arrive late is 34 percent.
There are 170 times late arrival of the plane out of the total 500.
Calculate the probability that a flight departs late or arrives early.
The late departs of flights are 27 percent. There are 137 flights which departed late. The early arrival of flights is 58.2 percent.
Assume now that the random variable X=Arrival Time is exactly normally distributed with mean m= -2.5 and standard deviation s= 23. Compute the probability of a flight arriving late based on this new information. Does this contradict your answer from Part 8?
X= 170 the number of flights in time.
The formula is :
P (X) = x – m/sd
P (x)= 7.5
Yes the answers contradict with each others.
Write a brief description (250–500 words) of the data set including the discussion required in the points above.
The data was regarding the flights timing at the airport. The timings of both departure and arrival of planes. The given data was the record of 2nd-week flights details. The data are given shows that there are a lot of flights which got late in both arrivals and departure. The highest values of the given data of departure are -24 hours which shows the 24 hours early arrival. While the plane was departed late by 23 hours according to the data. The mean of the data of departs flights were -5.269. There were total 500 flights in the week, and the total number of late departs were 137 out of 500. While before in time departure were 36, and early departs were 327 flights. The probability shows us the chances of an outcome. For instance the head and tail in tossing the coin. Random variable X is the variable which has some possible outcomes in numerical terms of a random phenomenon. Random variables are of two types known as the discrete and random variables. A binomial distribution is very different from a normal distribution, and yet if the sample size is large enough, the shapes will be quite similar. The key difference is that a binomial distribution is discrete, not continuous (Tuckwell, 2018). In other words, it is impossible to find a data value between any two data values. Probability measures the chance of a given event occurring. It is a number between 0 and 1 where a probability of 1 is an event that is certain to occur and a probability of 0 represents an event that cannot occur (Probability distributions. (2019).).
Probability Distributions. (2019). Erm.ecs.soton.ac.uk. Retrieved 12 March 2019, from http://www.erm.ecs.soton.ac.uk/theme7/probability_distributions.html
Shirazi, M., Lord, D., Dhavala, S. S., & Geedipally, S. R. (2016). A semiparametric negative binomial generalized linear model for modeling over-dispersed count data with a heavy tail: characteristics and applications to crash data. Accident Analysis & Prevention, 91, 10-18.
Tuckwell, H. C. (2018). Elementary applications of probability theory. Routledge.
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