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kondaur [170]
3 years ago
5

Find the distance between the two points rounding to the nearest tenth (if necessary). (−2,−2) and (0,7)​

Mathematics
1 answer:
pogonyaev3 years ago
7 0

Answer:

9 units

Step-by-step explanation:

(0,7)-(-2,-2)= (2,9)

distance = 9 units

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Ive been waiting hours for someone to answer this question. Is everyone an idiot.
stepan [7]

Answer:

D) x = -2/3 and x = 6

Step-by-step explanation:

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4x² + −4x + 8 + −x² + −12x + −20           Combine like terms            

(4x² + −x²) + (−4x + −12x) + (8 + −20)      Combine like terms

3x² −16x −12 = 0      

x = -2/3 and x = 6

I graphed the equation on the graph below.

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Norma-Jean [14]
9) 6/10
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Use the associative property to identify which expression is equal to (10)(5x)(7)
ludmilkaskok [199]
The answer is B. common cold 


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PLEASE HELP!! WILL REQARD A LOT
Soloha48 [4]

9514 1404 393

Answer:

  17,500

Step-by-step explanation:

The problem can be written as a proportion:

  hits/revenue = 1000/(£2) = x/(£35)

Multiplying by £35 gives ...

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Read 2 more answers
In your own words, explain the Normal Distribution and provide a real world example.
elena-14-01-66 [18.8K]

Answer:

The Normal distribution is a continuous probability distribution with possible values all the reals. Some properties of this distribution are:

Is symmetrical and bell shaped no matter the parameters used. Usually if X is a random variable normally distributed we write this like that:

X \sim N(\mu , \sigma)

The two parameters are:

\mu who represent the mean and is on the center of the distribution

\sigma who represent the standard deviation  

One particular case is the normal standard distribution denoted by:

Z \sim N(0,1)

Example: Usually this distribution is used to model almost all the practical things in the life one of the examples is when we can model the scores of a test. Usually the distribution for this variable is normally distributed and we can find quantiles and probabilities associated

Step-by-step explanation:

The Normal distribution is a continuous probability distribution with possible values all the reals. Some properties of this distribution are:

Is symmetrical and bell shaped no matter the parameters used. Usually if X is a random variable normally distributed we write this like that:

X \sim N(\mu , \sigma)

The two parameters are:

\mu who represent the mean and is on the center of the distribution

\sigma who represent the standard deviation  

One particular case is the normal standard distribution denoted by:

Z \sim N(0,1)

Example: Usually this distribution is used to model almost all the practical things in the life one of the examples is when we can model the scores of a test. Usually the distribution for this variable is normally distributed and we can find quantiles and probabilities associated

4 0
3 years ago
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