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Airida [17]
2 years ago
14

10 fish are in a tank, 3 fish drown, how many fish are left?

Mathematics
2 answers:
taurus [48]2 years ago
6 0
10. Fish don't drown (; nice try tho
alukav5142 [94]2 years ago
5 0

Answer:

10

Step-by-step explanation:

fish can't drown dude

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PLEASE ANSWER THIS QUICKLY I NEED THIS DONE BY 3:45!!!!!
salantis [7]

Answer:

Ok. Listen. I don't want you to cram, or ask for help at the last minute, but we will take it step by step.

Step-by-step explanation:

1. (1.6)/100=0.016

2. 2/0.016=125

3. 25% error.

Do you see why?

7 0
3 years ago
An interior automotive supplier places several electrical wires in a harness.Apull test measures the force required to pull spli
oksano4ka [1.4K]

Answer:

a) For this case we can use the following R code to construct the qq plot

> data<-c(28.8, 24.4, 30.1, 25.6, 26.4, 23.9, 22.1, 22.5, 27.6, 28.1, 20.8, 27.7, 24.4, 25.1, 24.6, 26.3, 28.2, 22.2, 26.3, 24.4)

# The above line is in order to store the data in a vector

> qqnorm(data, pch = 1, frame = FALSE)

# The line above is in order to calculate the quantiles from the data assumin Normal distribution

> qqline(data, col = "steelblue", lwd = 2)

# The line above is in order to put a line for the theoretical dsitribution

The result is on the figure attached.

b) For this case as we can see on the figure attached the calculated quantiles are not far from the theorical quantiles given byt the straaigth blue line so then we can conclude that the distribution seems to be normal.

Step-by-step explanation:

For this case we have the following data:

28.8, 24.4, 30.1, 25.6, 26.4, 23.9, 22.1, 22.5, 27.6, 28.1, 20.8, 27.7, 24.4, 25.1, 24.6, 26.3, 28.2, 22.2, 26.3, 24.4

The quantile-quantile or q-q plot is a graphical procedure in order to check the validity of a distributional assumption for a data set. We just need to calculate "the theoretically expected value for each data point based on the distribution in question".

If the values are asusted to the assumed distribution, we will see that "the points on the q-q plot will fall approximately on a straight line"

For this case our distribution assumed is normal.

Part a

For this case we can use the following R code to construct the qq plot

> data<-c(28.8, 24.4, 30.1, 25.6, 26.4, 23.9, 22.1, 22.5, 27.6, 28.1, 20.8, 27.7, 24.4, 25.1, 24.6, 26.3, 28.2, 22.2, 26.3, 24.4)

# The above line is in order to store the data in a vector

> qqnorm(data, pch = 1, frame = FALSE)

# The line above is in order to calculate the quantiles from the data assuming Normal distribution (0,1)

> qqline(data, col = "steelblue", lwd = 2)

# The line above is in order to put a line for the theoretical distribution

The result is on the figure attached.

Part b

For this case as we can see on the figure attached the calculated quantiles are not far from the theorical quantiles given byt the straaigth blue line so then we can conclude that the distribution seems to be normal.

4 0
3 years ago
Create an equivalent expression that includes a set of parentheses that make the value of the expression 5. Remember, you can ha
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Alan has forgotten his 4-digit PIN code.
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Answer:

a lot of different answers

Step-by-step explanation:

8 0
3 years ago
Read 2 more answers
If f(x) = - 4x and g(x)= 5x?, find<br> a. (f+g)(x)<br> b.(-9)(x)<br> C. (fog)(x)
o-na [289]

a) (f+g)(x) = f(x) + g(x) = -4x + 5x = x

b) -9(x) = -9x? I have no idea what this means.

c) (f o g)(x) = (f(g(x)) = f(5x) = -4 (5x) = -20x

8 0
3 years ago
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