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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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Test of p=0.94 vs p≠0.94
Sample	X	N	Sample p	95% CI	Z-Value	P-Value
1	1957	2,136	0.916199	(0.904448,0.927949)	−4.63	0.000

Question content area bottom

Part 1

a. Is the test two-tailed, left-tailed, or right-tailed?

Two-tailed test

Your answer is correct.

Right tailed test

Left-tailed test

Part 2

b. What is the test statistic?

The test statistic is

negative 3.89−3.89.

(Round to two decimal places as needed.)

Part 3

c. What is the P-value?

The P-value is

0.0000.000.

(Round to three decimal places as needed.)

Part 4

d. What is the null hypothesis and what do you conclude about it?

Identify the null hypothesis.

A.Upper H 0 : p equals 0.94

H0: p=0.94

Your answer is correct.

B.Upper H 0 : p less than 0.94

H0: p<0.94

C.Upper H 0 : p greater than 0.94

H0: p>0.94

D.Upper H 0 : p not equals 0.94

H0: p≠0.94

Part 5

Choose the correct answer below.

Reject

the null hypothesis because the P-value is

greater than

the significance level,

α.

Reject

the null hypothesis because the P-value is

less than or equal to

the significance level,

α.

Fail to reject

the null hypothesis because the P-value is

greater than

the significance level,

α.

Fail to reject

the null hypothesis because the P-value is

less than or equal to

the significance level,

α.

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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