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Korolek [52]
4 years ago
10

18 12/19+ 23 18/19- 15 14/19 simplify with explanation. please help.!

Mathematics
1 answer:
Lana71 [14]4 years ago
4 0
I got the answer of

26 107/152

im sure it correct but hope this helps have a good day
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A sign along the road has these measurements. What is the area of the sign? 35 and 20
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Answer: 700

Step-by-step explanation:

35 x 20 = 700

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(2x^2y+14xy)-(-8x^2y-9xy+5)
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Abnormalities In the 1980s, it was generally believed that congenital abnormalities affected about 5% of the nation’s chil-dren.
Ainat [17]

Answer:

Null hypothesis:p\leq 0.05  

Alternative hypothesis:p>0.05

The conditions and requirements are explained on detail below.

Step-by-step explanation:

1) Data given and notation n  

n=384 represent the random sample taken  

X=46 represent the children with abnormalities in the sample

\hat p=\frac{46}{384}=0.120 estimated proportion of children with abnormalities in the sample

p_o=0.05 is the value that we want to test  

\alpha represent the significance level (no given)  

z would represent the statistic (variable of interest)  

p_v represent the p value (variable of interest)  

p= proportion of children with congenital abnormalities

2) Concepts and formulas to use  

We need to conduct a hypothesis in order to test the claim that the proportion of children with congenital abnormalities exceeds 5%. :  

Null hypothesis:p\leq 0.05  

Alternative hypothesis:p>0.05

We assume that the proportion follows a normal distribution.  

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,higher or less) from a hypothesized value p_o.  

<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_o =384*0.05=19.2>10

n(1-p_o)=384*(1-0.05)=364.8>10

3) Calculate the statistic  

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

z=\frac{0.120 -0.05}{\sqrt{\frac{0.05(1-0.05)}{384}}}=6.29

4) 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 is not provided usually is \alpha=0.05. The next step would be calculate the p value for this test.  

Since is a one side test the p value would be:  

p_v =P(z>6.29)=1.59x10^{-10}  

Based on the p value obtained and using the significance level given \alpha=0.05 we have p_v so we can conclude that we have enough evidence to reject the null hypothesis, and we can said that at 5% of significance the proportion of children with abnormalities exceeds 0.05 or 5% .  

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4 years ago
Two researchers run identical experiments except researcher A collects twice as many points as researcher B. For a specific valu
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Answer:

B

Step-by-step explanation:

Consider an event A happening. If we do not have enough data to estimate its actual probability, we may choose a range 0.6 to 0.9 as a first case which indicates we are quite sure it will most likely occur. If however, we have enough data, we may estimate a range of 0.7 to 0.8 as a second case that is more certain on its actual likelihood of occurrence.

Say the actual probability of the event is given as 0.75, in the first case, we can infer the probability interval as 0.75 ± 0.15 (as 0.75-0.15=0.6 and 0.75+0.15=0.9 for the lower and upper bounds respectively). In the second case, we can infer the probability interval as 0.75±0.05 (as 0.75-0.05=0.7 and 0.75+0.05=0.8 for the lower and upper bounds respectively).

Thus, we can see that with more certainty of the event happening (with more data in this case), the probability or prediction intervals are lower.

Hence, in the experiment, we will observe a narrower prediction interval for researcher A who has more (twice as many points) data than researcher B who has fewer points.

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