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andrezito [222]
1 year ago
12

Unit 2 Show Your Work, BYU Geometry Part 1

Mathematics
1 answer:
irga5000 [103]1 year ago
3 0

The true statements about ∠MJK and ∠MJL are

  • The sum of the two angles is 180 degrees
  • JK and JL are opposite rays
  • JK and JL form a straight line

<h3>How to determine the true statements?</h3>

The statement is given as:

∠MJK and ∠MJL are a linear pair of angles and the angles are also supplementary

As a general rule, linear pair angles are adjacent angles that add up to 180 degrees. This means that:

  • They are supplementary angles
  • They have a sum of 180 degrees
  • The angles may or may not be congruent
  • They have opposite rays i.e. straight line

The above means that, the following options are true:

(e), (f) and (h)

Also, the converse of the statement is:

∠MJK and ∠MJL are supplementary angles and the angles are also  linear pair of angles

This is false because not all supplementary angles are  linear pair angles

Read more about linear pair angles at:

brainly.com/question/1511741

#SPJ1

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Historically, the proportion of people who trade in their old car to a car dealer when purchasing a new car is 48%. Over the pre
artcher [175]

Answer: The proportion of new car buyers that trade in their old car has statistically significantly decreased.

Step-by-step explanation:

Since we have given that

p = 48% = 0.48

n = 115

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So, \hat{p}=\dfrac{46}{115}=0.40

So, hypothesis would be

H_0:\ p=\hat{p}\\\\H_a:p

So, test value would be

z=\dfrac{p-\hat{p}}{\sqrt{\dfrac{p(1-p)}{n}}}\\\z=\dfrac{0.48-0.40}{\sqrt{\dfrac{0.48\times 0.52}{115}}}\\\\z=\dfrac{0.08}{0.0466}\\\\z=1.72

At 10% level of significance, critical value would be

z= 1.28

Since 1.28 < 1.72

So, we will reject the null hypothesis.

Hence, the proportion of new car buyers that trade in their old car has statistically significantly decreased.

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What is the likelihood that a fair coin will land heads or tails?
Marina CMI [18]

Answer:

I believe it is 0.5

Step-by-step explanation:

If you flip a normal coin (called a “fair” coin in probability parlance), you normally have no way to predict whether it will come up heads or tails. Both outcomes are equally likely. There is one bit of uncertainty; the probability of a head, written p(h), is 0.5 and the probability of a tail (p(t)) is 0.5. The sum of the probabilities of all the possible outcomes adds up to 1.0, the number of bits of uncertainty we had about the outcome before the flip. Since exactly one of the four outcomes has to happen, the sum of the probabilities for the four possibilities has to be 1.0. To relate this to information theory, this is like saying there is one bit of uncertainty about which of the four outcomes will happen before each pair of coin flips. And since each combination is equally likely, the probability of each outcome is 1/4 = 0.25. Assuming the coin is fair (has the same probability of heads and tails), the chance of guessing correctly is 50%, so you'd expect half the guesses to be correct and half to be wrong. So, if we ask the subject to guess heads or tails for each of 100 coin flips, we'd expect about 50 of the guesses to be correct. Suppose a new subject walks into the lab and manages to guess heads or tails correctly for 60 out of 100 tosses. Evidence of precognition, or perhaps the subject's possessing a telekinetic power which causes the coin to land with the guessed face up? Well,…no. In all likelihood, we've observed nothing more than good luck. The probability of 60 correct guesses out of 100 is about 2.8%, which means that if we do a large number of experiments flipping 100 coins, about every 35 experiments we can expect a score of 60 or better, purely due to chance.

6 0
3 years ago
Read 2 more answers
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