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OverLord2011 [107]
3 years ago
11

Which equation is for a line passing through (-2,3) and (3,4)?

Mathematics
1 answer:
Bad White [126]3 years ago
3 0

Answer:

I need help on this to what is it

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2 + 7c - 4c2 – 3c + 4
iragen [17]

Answer:

-4c² + 4c +6

Step-by-step explanation:

combine like terms

2+ 4 = 6

7c - 3c = 4c

- 4c

Hope this helps!

8 0
3 years ago
Read 2 more answers
Please help ASAP!!!! Will give brainliest!!!
ElenaW [278]

Answer: 0

Step-by-step explanation: it said round to the nearest hundredth when you just need 0.21 so 0.21 rounded to the nearest hundredth is going to be 0.

5 0
3 years ago
Which equation could be used to find the nth term in the sequence below?
Pachacha [2.7K]

Answer:

a

Step-by-step explanation:

The sequence jumps by intervals of 3 each time. This tells us that the answer has 3n in it. Then, we know that the first value is two, which is 1 less than 3. This means the answer is a, 3n-1.

8 0
3 years ago
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A rocket is launched from the top of a 76-foot cliff with an initial velocity of 135 ft/s.a. Substitute the values into the vert
mixas84 [53]
You divide -16 by 76 and then mutiply your answer by the  number you got
3 0
4 years ago
Read 2 more answers
Explain type i error and give an example. explain type ii error and give an example. what is the best way to reduce both kinds o
Leviafan [203]

Type I error says that we suppose that the null hypothesis exists rejected when in reality the null hypothesis was actually true.

Type II error says that we suppose that the null hypothesis exists taken when in fact the null hypothesis stood actually false.

<h3>What is Type I error and Type II error?</h3>

In statistics, a Type I error exists as a false positive conclusion, while a Type II error exists as a false negative conclusion.

Making a statistical conclusion still applies uncertainties, so the risks of creating these errors exist unavoidable in hypothesis testing.

The probability of creating a Type I error exists at the significance level, or alpha (α), while the probability of making a Type II error exists at beta (β). These risks can be minimized through careful planning in your analysis design.

Examples of Type I and Type II error

  • Type I error (false positive): the testing effect says you have coronavirus, but you actually don’t.
  • Type II error (false negative): the test outcome says you don’t have coronavirus, but you actually do.

To learn more about Type I and Type II error refer to:

brainly.com/question/17111420

#SPJ4

7 0
2 years ago
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