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AURORKA [14]
3 years ago
7

F(x) = x2+3x+2 x2+5x+4 Why is there no zero at x = –1?

Mathematics
1 answer:
dmitriy555 [2]3 years ago
7 0
You could rewrite F(x) as

\dfrac{x^2+3x+2}{x^2+5x+4}=\dfrac{(x+1)(x+2)}{(x+1)(x+4)}

and be tempted to cancel out the factors of x+1. But this cancellation is only valid when x\neq-1.

When x=-1, you end up with the indeterminate form \dfrac00, which is why -1 is not a zero.
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Step-by-step explanation:

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In a recent​ year, a poll asked 2362 random adult citizens of a large country how they rated economic conditions. In the​ poll,
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Answer:

a) The 99% confidence interval is given by (0.198;0.242).

b) Based on the p value obtained and using the significance level assumed \alpha=0.01 we have p_v>\alpha so we can conclude that we fail to reject the null hypothesis, and we can said that at 1% of significance the proportion of people who are rated with Excellent/Good economy conditions not differs from 0.24. The interval also confirms the conclusion since 0.24 it's inside of the interval calculated.

c) \alpha=0.01

Step-by-step explanation:

<em>Data given and notation   </em>

n=2362 represent the random sample taken

X represent the people who says that  they would watch one of the television shows.

\hat p=\frac{X}{n}=0.22 estimated proportion of people rated as​ Excellent/Good economic conditions.

p_o=0.24 is the value that we want to test

\alpha represent the significance level  

z would represent the statistic (variable of interest)

p_v represent the p value (variable of interest)  <em> </em>

<em>Concepts and formulas to use   </em>

We need to conduct a hypothesis in order to test the claim that 24% of people are rated with good economic conditions:  

Null hypothesis:p=0.24  

Alternative hypothesis:p \neq 0.24  

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.

Part a: Test the hypothesis

<em>Check for the assumptions that he sample must satisfy in order to apply the test   </em>

a)The random sample needs to be representative: On this case the problem no mention about it but we can assume it.  

b) The sample needs to be large enough

np = 2362x0.22=519.64>10 and n(1-p)=2364*(1-0.22)=1843.92>10

Condition satisfied.

<em>Calculate the statistic</em>  

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

z=\frac{0.22 -0.24}{\sqrt{\frac{0.24(1-0.24)}{2362}}}=-2.28

The confidence interval would be given by:

\hat p \pm z_{\alpha/2}\sqrt{\frac{\hat p (1-\hat p)}{n}}

The critical value using \alpha=0.01 and \alpha/2 =0.005 would be z_{\alpha/2}=2.58. Replacing the values given we have:

0.22 - (2.58)\sqrt{\frac{0.22(1-0.22)}{2362}}=0.198

 0.22 + (2.58)\sqrt{\frac{0.22(1-0.22)}{2362}}=0.242

So the 99% confidence interval is given by (0.198;0.242).

Part b

<em>Statistical decision   </em>

P value method or p value approach . "This method consists on 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 is \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  

So based on the p value obtained and using the significance level assumed \alpha=0.01 we have p_v>\alpha so we can conclude that we fail to reject the null hypothesis, and we can said that at 1% of significance the proportion of people who are rated with Excellent/Good economy conditions not differs from 0.24. The interval also confirms the conclusion since 0.24 it's inside of the interval calculated.

Part c

The confidence level assumed was 99%, so then the signficance is given by \alpha=1-confidence=1-0.99=0.01

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