Can the normal distribution be used ? Compute P(X) using the binomial probability formula. Then determine whether the normal distribution can be used to estimate this probability If so, approsinateP0) using the normal distribution and compare the result with the exact probability.n 58, p=0.5, and X 40For n=58, p= 0.5, and X= 40, find P(X).POX) = 0016 (Round to four decimal places as needed.)Can the normal distribution be used to approximate this probability?O A. Yes, the normal distribution can be used because np(1- p)2 10.O B. Yes, the normal distribution can be used because np(1-p)s 10.O C. No, the normal distribution cannot be used because np(1- p)2 10.O D. No, the normal distribution cannot be used because np(1- p) < 10Clear allFinal checkCopyright © 2022 Pearson Education Inc. All rights reserved. Tems ofUsehp

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Answered: Compute P(X) using the binomial…

Calculus For The Life Sciences

2nd Edition

ISBN:9780321964038

Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.

Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.

Chapter13: Probability And Calculus

Section13.3: Special Probability Density Functions

Problem 26E

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Can the normal distribution be used ?

Compute P(X) using the binomial probability formula. Then determine whether the normal distribution can be used to estimate this probability If so, approsinate
P0) using the normal distribution and compare the result with the exact probability.
n 58, p=0.5, and X 40
For n=58, p= 0.5, and X= 40, find P(X).
POX) = 0016 (Round to four decimal places as needed.)
Can the normal distribution be used to approximate this probability?
O A. Yes, the normal distribution can be used because np(1- p)2 10.
O B. Yes, the normal distribution can be used because np(1-p)s 10.
O C. No, the normal distribution cannot be used because np(1- p)2 10.
O D. No, the normal distribution cannot be used because np(1- p) < 10
Clear all
Final check
Copyright © 2022 Pearson Education Inc. All rights reserved. Tems ofUse
hp

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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