Answered: height of basketball players in a…

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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The height of basketball players in a country town club in Victoria follows a normal distribution with a mean of 168cm and a standard deviation of 6 cm.

What is the probability that the average height of 25 randomly selected basketball players in the club is more than 167cm?

(A) How would you justify the assumption for the above test to be valid: Please choose from below

- The points are randomly scattered around a straight line, so the assumptions of linearity and constant spread are satisfied. The shape of the histogram indicates that the residuals come from a normal distribution

- None of these

- The shape of the histogram indicates that the scores may be from a normal distribution and/or the sample size is reasonably large so the sample mean will be from a normal population

- Since nπ and n(1-π) are both ≥5, the Central Limit Theorem applies and we can assume that p arises from a normal distribution

-Since the population is normal, therefore sample means follow a normal distribution

-The boxplots indicate that the samples are fairly symmetric and the spreads are similar, so we can assume both sets of data come from normal distributions with the same spread

(B) Enter the value of the test statistic (2 decimal places)

(C) What proportion of basketball players is taller than 167cm in a group of 25 players in the club (provide your answers between a value 0 to 1 and for 4 decimal places)

Features Features Normal distribution is characterized by two parameters, mean (µ) and standard deviation (σ). When graphed, the mean represents the center of the bell curve and the graph is perfectly symmetric about the center. The mean, median, and mode are all equal for a normal distribution. The standard deviation measures the data's spread from the center. The higher the standard deviation, the more the data is spread out and the flatter the bell curve looks. Variance is another commonly used measure of the spread of the distribution and is equal to the square of the standard deviation.

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