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stiv31 [10]
3 years ago
12

What is the missing step in the proof?

Mathematics
2 answers:
ollegr [7]3 years ago
3 0
The correct answer to this is the option:
C. Statement: ∠GJI≅∠JLK
Reason: For parallel lines cut by a transversal, corresponding angles are congruent.

Since the two angles are corresponding angles and the two parallel lines are cut by a transversal.
nadya68 [22]3 years ago
3 0
The <u>correct answer</u> is:

D) <span>Statement: ∠GJI and ∠IJL are supplementary; Reason: Linear Pair Theorem 

Explanation:

Step 3 states "</span><span>∠IKL≅∠GJI Transitive Property of Equality".  
Step 5 states "</span><span>∠IKL and ∠IJL are supplementary. Transitive Property of Equality".

The inferred piece of information here is that </span>∠GJI and ∠IJL are supplementary.  Since we do not have anything relating ∠IKL and ∠IJL, we must have something relating the two; relating ∠GJI and ∠IJL will work, since we know that ∠IKL≅∠GJI.
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a function f(x) is described by the ordered pair (x,y). if given the function is translated 2 units to the right and then reflec
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3 years ago
A researcher reports survey results by stating that the standard error of the mean is 25 the population standard deviation is 40
bezimeni [28]

Answer:

a) A sample of 256 was used in this survey.

b) 45.14% probability that the point estimate was within ±15 of the population mean

Step-by-step explanation:

This question is solved using the normal probability distribution and the central limit theorem.

Normal probability distribution

When the distribution is normal, we use the z-score formula.

In a set with mean \mu and standard deviation \sigma, the zscore of a measure X is given by:

Z = \frac{X - \mu}{\sigma}

The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the pvalue, we get the probability that the value of the measure is greater than X.

Central Limit Theorem

The Central Limit Theorem estabilishes that, for a normally distributed random variable X, with mean \mu and standard deviation \sigma, the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean \mu and standard deviation s = \frac{\sigma}{\sqrt{n}}.

For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.

a. How large was the sample used in this survey?

We have that s = 25, \sigma = 400. We want to find n, so:

s = \frac{\sigma}{\sqrt{n}}

25 = \frac{400}{\sqrt{n}}

25\sqrt{n} = 400

\sqrt{n} = \frac{400}{25}

\sqrt{n} = 16

(\sqrt{n})^2 = 16^2[tex][tex]n = 256

A sample of 256 was used in this survey.

b. What is the probability that the point estimate was within ±15 of the population mean?

15 is the bounds with want, 25 is the standard error. So

Z = 15/25 = 0.6 has a pvalue of 0.7257

Z = -15/25 = -0.6 has a pvalue of 0.2743

0.7257 - 0.2743 = 0.4514

45.14% probability that the point estimate was within ±15 of the population mean

3 0
3 years ago
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