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Sergeeva-Olga [200]
3 years ago
7

Which set of ordered pairs does not represent a function? O {(4, -6), (-5,5),(-6,0), (7,0)} O {(0, -4), (9,6), (2,8), (8,8)} O {

(8,-6), (9,-8), (2,4), (3, -6)} o {(-5,2), (3,-9),(-1,8), (-5,9)}​
Mathematics
1 answer:
Ipatiy [6.2K]3 years ago
7 0

Function is a set of ordered pairs that do not have same domain or x-value. That means in a set of ordered pairs, the domain cannot have same value.

The only relation that isn't function here is the last choice because in a set of ordered pairs, there are two -5's which is repetitive. Other choices do not have same x-values in one set except for the last choice.

Answer

  • {(-5,2),(3,-9),(-1,8),(-5,9)}

Hope this helps and let me know if you have any doubts!

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The marketing manager for an automobile manufacturer is interested in determining the proportion of new compact-car owners who w
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Answer:

z=\frac{0.395 -0.3}{\sqrt{\frac{0.3(1-0.3)}{200}}}=2.932  

p_v =2*P(z>2.932)=0.0034  

So the p value obtained was a very low value and using the significance level given \alpha=0.01 we have p_v so we can conclude that we have enough evidence to reject the null hypothesis, and we can said that at 1% of significance the proportion of people that they would have purchased the GPS navigation system is significantly different from 0.3

Step-by-step explanation:

Data given and notation

n=200 represent the random sample taken

X=79 represent the  number of people that they would have purchased the GPS navigation system

\hat p=\frac{79}{200}=0.395 estimated proportion of people that they would have purchased the GPS navigation system

p_o=0.3 is the value that we want to test

\alpha=0.01 represent the significance level

Confidence=99% or 0.99

z would represent the statistic (variable of interest)

p_v represent the p value (variable of interest)  

Concepts and formulas to use  

We need to conduct a hypothesis in order to test the claim that the true proportion is 0.3 or no.:  

Null hypothesis:p=0.3  

Alternative hypothesis:p \neq 0.3  

When we conduct a proportion test we need to use the z statistic, and the is given by:  

z=\frac{\hat p -p_o}{\sqrt{\frac{p_o (1-p_o)}{n}}} (1)  

The One-Sample Proportion Test is used to assess whether a population proportion \hat p is significantly different from a hypothesized value p_o.

Calculate the statistic  

Since we have all the info requires we can replace in formula (1) like this:  

z=\frac{0.395 -0.3}{\sqrt{\frac{0.3(1-0.3)}{200}}}=2.932  

Statistical decision  

It's important to refresh the p value method or p value approach . "This method is about determining "likely" or "unlikely" by determining the probability assuming the null hypothesis were true of observing a more extreme test statistic in the direction of the alternative hypothesis than the one observed". Or in other words is just a method to have an statistical decision to fail to reject or reject the null hypothesis.  

The significance level provided \alpha=0.01. The next step would be calculate the p value for this test.  

Since is a bilateral test the p value would be:  

p_v =2*P(z>2.932)=0.0034  

So the p value obtained was a very low value and using the significance level given \alpha=0.01 we have p_v so we can conclude that we have enough evidence to reject the null hypothesis, and we can said that at 1% of significance the proportion of people that they would have purchased the GPS navigation system is significantly different from 0.3

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