’m really confused on the last part, is the z statistics is not in or in the critical region and the researcher can or cannot reject the null hypothesis please help me

MATLAB: An Introduction with Applications
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Author:Amos Gilat
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I’m really confused on the last part, is the z statistics is not in or in the critical region and the researcher can or cannot reject the null hypothesis please help me
To use the tool to help you evaluate the hypothesis, click on the icon with the purple line, place the two orange lines on the critical values, and then
place the purple line on the z statistic.
Standard Normal Distribution
Mean = 0.0
Standard Deviation = 1.0
NA
The critical region is
Oz 1.96 and z = -1.96
The Z-score boundaries for an alpha level a = 0.001 are:
Oz= 3.29 and z= -3.29
-4
O z = 2.58 and z = -2.58
-3
-2
2500
€5000
-1
-0.67
0
0.67
2500
1
2
3
4
Suppose that the calculated z statistic for a particular hypothesis test is 4.12 and the alpha is 0.001. This z statistic is
Therefore, the researcher
reject the null hypothesis, and he
Z
the critical region.
conclude the alternative hypothesis is probably correct.
Transcribed Image Text:To use the tool to help you evaluate the hypothesis, click on the icon with the purple line, place the two orange lines on the critical values, and then place the purple line on the z statistic. Standard Normal Distribution Mean = 0.0 Standard Deviation = 1.0 NA The critical region is Oz 1.96 and z = -1.96 The Z-score boundaries for an alpha level a = 0.001 are: Oz= 3.29 and z= -3.29 -4 O z = 2.58 and z = -2.58 -3 -2 2500 €5000 -1 -0.67 0 0.67 2500 1 2 3 4 Suppose that the calculated z statistic for a particular hypothesis test is 4.12 and the alpha is 0.001. This z statistic is Therefore, the researcher reject the null hypothesis, and he Z the critical region. conclude the alternative hypothesis is probably correct.
4. Alpha level and the critical region
The alpha level is set
Use the following Distributions tool to identify the boundaries that separate the extreme samples from the samples that are more obviously consistent
with the null hypothesis. Assume the null hypothesis is nondirectional, meaning that the critical region is split across both tails of the distribution.
The Z-score boundaries at an alpha level a = .05 are:
O z 3.29 and z = -3.29
O z = 2.58 and z = -2.58
the analysis of the data.
O z = 1.96 and z = -1.96
To use the tool to identify the z-score boundaries, click on the icon with two orange lines, and slide the orange lines until the area in the critical region
equals the alpha level. Remember that the probability will need to be split between the two tails.
To use the tool to help you evaluate the hypothesis, click on the icon with the purple line, place the two orange lines on the critical values, and then
place the purple line on the z statistic.
Transcribed Image Text:4. Alpha level and the critical region The alpha level is set Use the following Distributions tool to identify the boundaries that separate the extreme samples from the samples that are more obviously consistent with the null hypothesis. Assume the null hypothesis is nondirectional, meaning that the critical region is split across both tails of the distribution. The Z-score boundaries at an alpha level a = .05 are: O z 3.29 and z = -3.29 O z = 2.58 and z = -2.58 the analysis of the data. O z = 1.96 and z = -1.96 To use the tool to identify the z-score boundaries, click on the icon with two orange lines, and slide the orange lines until the area in the critical region equals the alpha level. Remember that the probability will need to be split between the two tails. To use the tool to help you evaluate the hypothesis, click on the icon with the purple line, place the two orange lines on the critical values, and then place the purple line on the z statistic.
Expert Solution
Step 1: Overview

At alpha equals 0.001, the critical values are z = 3.29 and z = -3.29.

At alpha equals 0.05, the critical values are z = 1.96 and z = -1.96.

R-codes:

1> c(qnorm(0.001/2,lower.tail = FALSE), qnorm(0.001/2)) #critical  values at alpha = 0.001
2[1]  3.29  -3.29
3> c(qnorm(0.05/2,lower.tail = FALSE), qnorm(0.05/2)) #critical  values at alpha = 0.05
4[1]  1.96 -1.96
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