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Tpy6a [65]
3 years ago
11

What is the domain of the function f(x)=1x-3 -3 when the range is {-6. 0, 9}?

Mathematics
1 answer:
sineoko [7]3 years ago
5 0

Answer:

D : { -3, 3, 12}

Step-by-step explanation:

I would suggest substituting the range for the function and solving for x like so.

i.e -6 = 1x-3, -6 + 3 = 1x -3 + 3, -3 = x, <u>x = </u><u>-</u><u>3</u><u>.</u>

0 = 1x - 3, 0 + 3 = 1x - 3 + 3, 3 = x, <u>x = 3</u><u>.</u>

9 = 1x - 3, 9 + 3 = 1x -3 + 3, 12 = x, <u>x = 12</u>

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Step-by-step explanation:

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dezoksy [38]

Hi there!

I'm guessing you want to know what the equation of the slope-intercept line is.

The general equation for the slope-intercept line looks like this:

y = mx + b

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Since we know what the slope is and we have the coordinates of a point, you can replace the slope and the value of the coordinates "x" & "y" in the equation and solve for "b" in order to find the whole equation :

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Divide each side of the equation by -1

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4 0
4 years ago
1 point
Black_prince [1.1K]

Answer:

I would say the answer is A because its closest to the number 300

Step-by-step explanation:

i hope i helped if its wrong im sorry

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blondinia [14]

Answer:

The system of hypothesis for this case are:

Null hypothesis: \mu =150

Alternative hypothesis: \mu >150

A type of error I for this case would be reject the null hypothesis that the true mean is equal than 150 when actually is not true.

Step-by-step explanation:

Previous concepts

A hypothesis is defined as "a speculation or theory based on insufficient evidence that lends itself to further testing and experimentation. With further testing, a hypothesis can usually be proven true or false".  

The null hypothesis is defined as "a hypothesis that says there is no statistical significance between the two variables in the hypothesis. It is the hypothesis that the researcher is trying to disprove".  

The alternative hypothesis is "just the inverse, or opposite, of the null hypothesis. It is the hypothesis that researcher is trying to prove".  

Type I error, also known as a “false positive” is the error of rejecting a null  hypothesis when it is actually true. Can be interpreted as the error of no reject an  alternative hypothesis when the results can be  attributed not to the reality.  

Type II error, also known as a "false negative" is the error of not rejecting a null  hypothesis when the alternative hypothesis is the true. Can be interpreted as the error of failing to accept an alternative hypothesis when we don't have enough statistical power.  

Solution to the problem

The system of hypothesis for this case are:

Null hypothesis: \mu =150

Alternative hypothesis: \mu >150

A type of error I for this case would be reject the null hypothesis that the true mean is equal than 150 when actually is not true.

8 0
4 years ago
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