Answered: A genetic experiment involving peas…

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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A genetic experiment involving peas yielded one sample of offspring consisting of 416 green peas and 170 yellow peas. Use a 0.01 significance level to test the claim that under the same circumstances, 27%of offspring peas will be yellow. Identify the null hypothesis, alternative hypothesis, test statistic, P-value, conclusion about the null hypothesis, and final conclusion that addresses the original claim. Use the P-value method and the normal distribution as an approximation to the binomial distribution.

test statistic____

p-value____

What is the conclusion about the null hypothesis?

A.Fail to reject the null hypothesis because the P-value is greater than the significance level,α.

B.Reject the null hypothesis because the P-value is less than or equal to the significance level, α.

C.Fail to reject the null hypothesis because the P-value is less than or equal to the significance level,α.

D.Reject the null hypothesis because the P-value is greater than the significance level,α.

What is the final conclusion?

A.There is not sufficient evidence to warrant rejection of the claim that 27%

of offspring peas will be yellow.

B.There is not sufficient evidence to support the claim that less than 27%

of offspring peas will be yellow.

C.There is sufficient evidence to support the claim that less than 27%

of offspring peas will be yellow.

D.There is sufficient evidence to warrant rejection of the claim that 27%

of offspring peas will be yellow.

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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