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vfiekz [6]
3 years ago
8

Find the missing angles.

Mathematics
1 answer:
Feliz [49]3 years ago
7 0

Answer:

a=30  b=30 c= 75

Step-by-step explanation:

By inferring that all triangles are the same measurments, you can easily knock out A and B since it is the same angle that is labeled 30 degrees.

Now to find C you need to know that all angles of a triangle add up to 180 degrees. You also need to know that both bottom angles are the same angle measurement, indicated by the little circle in the angle.

So, For the middle triangle you can create an equation. 30(which was b) + 2c = 180

Subtract 30 from both sides and you are left with:

2c = 150

Divide by 2 on each side:

c= 75

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Geno started to evaluate 90 − (18 + −2)(−3), as shown. What should he do next?
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Let s be the sum of all digits used in writing all numbers from 1 to 1010.find s
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Engineers at a large automobile manufacturing company are trying to decide whether to purchase brand A or brand B tires for the
andre [41]

Answer:

s_p =\sqrt{\frac{(12 -1)(5100)^2 +(12-1)(5900)^2}{12 +12 -2}}=5514.526

t=\frac{(37900-39800)-0}{5514.526\sqrt{\frac{1}{12}+\frac{1}{12}}}}=-0.844  

p_v =2*P(t_{22}  

Comparing the p value with a significance level for example \alpha=0.1 we see that p_v>\alpha so we can conclude that we have enough evidence to FAIL to reject the null hypothesis, and we can conclude that the true means are not significantly different.  

Step-by-step explanation:

Data given and notation

\bar X_{A}=37900 represent the mean for A

\bar X_{B}=39800 represent the mean for B

s_{A}=5110 represent the sample standard deviation for A

s_{B}=5900 represent the sample standard deviation for B

n_{A}=12 sample size for the group A  

n_{B}=12 sample size for the group B

\alpha Significance level provided  

t would represent the statistic (variable of interest)  

Concepts and formulas to use  

We need to conduct a hypothesis in order to check if the means are equal or not, the system of  hypothesis would be:  

Null hypothesis:\mu_{A}-\mu_{B}= 0  

Alternative hypothesis:\mu_{A} - \mu_{B}\neq 0  

We don't have the population standard deviation's but we assume that the population deviation is equal for both populations, so we can apply a t test to compare means, and the statistic is given by:  

t=\frac{(\bar X_{A}-\bar X_{B})-\Delta}{s_p\sqrt{\frac{1}{n_{A}}+\frac{1}{n_{B}}}} (1)  

Where s_p represent the standard deviation pooled given by:

s_p =\sqrt{\frac{(n_A -1)s^2_A +(n_B -1)s^2_B}{n_A +n_B -2}}

s_p =\sqrt{\frac{(12 -1)(5100)^2 +(12-1)(5900)^2}{12 +12 -2}}=5514.526

t-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.  

With the info given we can replace in formula (1) like this:  

t=\frac{(37900-39800)-0}{5514.526\sqrt{\frac{1}{12}+\frac{1}{12}}}}=-0.844  

P value  

We need to find first the degrees of freedom given by:

df=n_A +n_{B}-2=12+12-2=22

Since is a two tailed test the p value would be:  

p_v =2*P(t_{22}  

Comparing the p value with a significance level for example \alpha=0.1 we see that p_v>\alpha so we can conclude that we have enough evidence to FAIL to reject the null hypothesis, and we can conclude that the true means are not significantly different.  

6 0
4 years ago
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