question 17The amount of coffee that people drink per day is normally distributed with a mean of 16 ounces and a standard deviation of 6 ounces. 15 randomly selected people are surveyed. Round all answers to 5 decimal places where possible.What is the distribution of ¯xx¯? ¯xx¯ ~ N(,)What is the probability that one randomly selected person drinks between 15.5 and 17.5 ounces of coffee per day? For the 15 people, find the probability that the average coffee consumption is between 15.5 and 17.5 ounces of coffee per day.

We have given that, X be the random variable from normal distribution with mean (μ) = 16, standard deviation (σ) = 6 and sample size (n) = 15 Then, We will find the above corresponding probabilities and distribution = ?

Answered: question 17 The amount of coffee that…

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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question 17

The amount of coffee that people drink per day is normally distributed with a mean of 16 ounces and a standard deviation of 6 ounces. 15 randomly selected people are surveyed. Round all answers to 5 decimal places where possible.

What is the distribution of ¯xx¯? ¯xx¯ ~ N(,)
What is the probability that one randomly selected person drinks between 15.5 and 17.5 ounces of coffee per day?
For the 15 people, find the probability that the average coffee consumption is between 15.5 and 17.5 ounces of coffee per day.

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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