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Natalka [10]
3 years ago
8

A salesman for a new manufacturer of cellular phones claims not only that they cost the retailer less but also that the percenta

ge of defective cellular phones found among his products, ( p1p1 ), will be no higher than the percentage of defectives found in a competitor's line, ( p2p2 ). To test this statement, the retailer took a random sample of 135135 of the salesman's cellular phones and 105105 of the competitor's cellular phones. The retailer found that 1515 of the salesman's cellular phones and 55 of the competitor's cellular phones were defective. Does the retailer have enough evidence to reject the salesman's claim? Use a significance level of α=0
Mathematics
1 answer:
Paraphin [41]3 years ago
4 0

Answer:

z=\frac{0.111-0.524}{\sqrt{0.292(1-0.292)(\frac{1}{135}+\frac{1}{105})}}=-6.985    

p_v =P(Z  

Comparing the p value with the significance level assumed \alpha=0.05 we see that p_v so we can conclude that we have enough evidence to reject the null hypothesis, and we can conclude that the proportion of defective for the manufacturer is lower than the proportion of defective for the competitor at 5% of significance

Step-by-step explanation:

Data given and notation  

X_{1}=15 represent the number of defective for the manufacturer

X_{2}=55 represent the number of defective for the competitor

n_{1}=135 sample for the manufacturer

n_{2}=105 sample for the competitor

p_{PC}=\frac{15}{135}=0.111 represent the proportion of defective for the manufacturer

p_{2}=\frac{55}{105}=0.524 represent the proportion of defective for the competitor

z would represent the statistic (variable of interest)  

p_v represent the value for the test (variable of interest)  

\alpha=0.05 significance level given

Concepts and formulas to use  

We need to conduct a hypothesis in order to check if p1<p2, the system of hypothesis would be:  

Null hypothesis:p_{1} - p_{2} \geq 0  

Alternative hypothesis:p_{1} - p_{2} < 0  

We need to apply a z test to compare proportions, and the statistic is given by:  

z=\frac{p_{1}-p_{2}}{\sqrt{\hat p (1-\hat p)(\frac{1}{n_{1}}+\frac{1}{n_{2}})}}   (1)  

Where \hat p=\frac{X_{1}+X_{2}}{n_{1}+n_{2}}=\frac{15+55}{135+105}=0.292  

z-test: Is used to compare group means. Is one of the most common tests and is used to determine whether the means of two groups are equal to each other.  

Calculate the statistic  

Replacing in formula (1) the values obtained we got this:  

z=\frac{0.111-0.524}{\sqrt{0.292(1-0.292)(\frac{1}{135}+\frac{1}{105})}}=-6.985    

Statistical decision

Since is a left tailed side test the p value would be:  

p_v =P(Z  

Comparing the p value with the significance level assumed \alpha=0.05 we see that p_v so we can conclude that we have enough evidence to reject the null hypothesis, and we can conclude that the proportion of defective for the manufacturer is lower than the proportion of defective for the competitor at 5% of significance

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