Answered: See pictures for question D and E and…

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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See pictures for question D and E and (A, B and Cand was submitted on a different question.

According to a report done by S & J Power, the mean lifetime of the light bulbs it manufactures is 44 months. A researcher for a consumer advocate group tests this by selecting 16 bulbs at random. For the bulbs in the sample, the mean lifetime is 47 months. It is known that the population standard deviation of the lifetimes is 5 months. Assume that the population is normally distributed. Can we conclude, at the 0.10 level of significance, that the population mean lifetime, μ, of light bulbs made by this manufacturer differs from 44 months?

Perform a two-tailed test. Then complete the parts below.

Carry your intermediate computations to three or more decimal places, and round your responses as specified below.

(a) State the null hypothesis H, and the alternative hypothesis H.
H, :0
H, :0
(b) Determine the type of test statistic to use.
(Choose one)▼
D=0
OSO
(c) Find the value of the test statistic. (Round to three or more decimal places.)
(d) Find the p-value. (Round to three or more decimal places.)
(e) Can we conclude that the population mean lifetime of light bulbs made by
this manufacturer differs from 44 months?
O Yes O No
S'

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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