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Irina18 [472]
3 years ago
5

Help fast asap plz.................

Mathematics
1 answer:
kolbaska11 [484]3 years ago
5 0

Answer:

2,4,6 and 8

Step-by-step explanation:

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Math stuff who ever answers right gets brainlest answer​
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Median is 83

Range: 61

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The median is the number in the middle.  The range is the max. number subtracted from the min number.

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2 years ago
YOUR TURN
Karolina [17]

Answer:

The equation is: <em>y</em> = 15·<em>x</em>

Step-by-step explanation:

It is provided that at the Speedy Bike Works, 15 bicycles are produced each hour.

Consider the table below.

                    Number of Hours:       1          3          6          10

Number of Bicycles Produced:      15        45        90       150

Compute the ratio of number of bicycles produced and number of hours for every data above as follows:

\text{1 hour}=\frac{15}{1}=15:1\\\\\text{3 hour}=\frac{45}{3}=\frac{15}{1}=15:1\\\\\text{6 hour}=\frac{90}{6}=\frac{15}{1}=15:1\\\\\text{10 hour}=\frac{150}{10}=\frac{15}{1}=15:1

The ratio of the number of bicycles produced and number of hours is same for every data value.

Thus, the relationship between the number of bicycles produced and number of hours is proportional.

The equation for the relationship is:

<em>y</em> = 15·<em>x</em>

<em>y</em> = number of bicycles produced

<em>x</em> = number of hours

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3 years ago
In a regression class, a student suggested the following: If extremely influential outlying cases are detected in a data set, si
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Answer:

Discarding the influential outlying cases when detected  is also known as flagging outliers in a data set, and this is because outliers do not follow the rest of the dataset's pattern. if this outliers are not discarded they would have a negative effect on any model attached to the dataset

Step-by-step explanation:

In a regression class ; If extremely influential outlying cases are detected in a Data set, discarding this influential outlying cases is the right way to go about it

Discarding the influential outlying cases when detected  is also known as flagging outliers in a data set, and this is because outliers do not follow the rest of the dataset's pattern. if this outliers are not discarded they would have a negative effect on any model attached to the dataset

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3 years ago
What is the strongest relationship you can infer between two variables from this statement?
Dmitry [639]

Answer:

hope this helps you with your question

3 0
3 years ago
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