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Liula [17]
3 years ago
5

Is 60n + 12 equal to 12(5n + 1)

Mathematics
1 answer:
hram777 [196]3 years ago
5 0

Answer: no

Step-by-step explanation:

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The circumference of the blue circle is about: 20 cm
The circumference of the orange circle is about: 10 cm
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3 years ago
Write the equation of the line graphed below in slope-intercept form (y=mx+b)?​
ioda
Y = -1/2x - 1

Tell me if you need explanation
7 0
3 years ago
Monique bought a shirt for $22.80 during a 30% off sale. How much does the shirt cost when it is not on sale?
spin [16.1K]

Answer:

29.64

Step-by-step explanation:

First we multiply 0.3 with 22.8.This will give us 6.84 which is the 30% off.Now we add 22.80 and 6.84 and we will get our answer 29.64.

4 0
2 years ago
Read 2 more answers
If a = 12 and B = 43 degrees, find c.
ludmilkaskok [199]

Answer:

74

Step-by-step explanation:

You subtract 43 from 12 you get 31 then you add 43 and 31 you get 74 bye

8 0
3 years ago
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Suppose each of the following data sets is a simple random sample from some population. For each dataset, make a normal QQ plot.
adell [148]

Answer:

a) For this case the histogram is not too skewed and we can say that is approximately symmetrical so then we can conclude that this dataset is similar to a normal distribution

b) For this case the data is skewed to the left and we can't assume that we have the normality assumption.

c) This last case the histogram is not symmetrical and the data seems to be skewed.

Step-by-step explanation:

For this case we have the following data:

(a)data = c(7,13.2,8.1,8.2,6,9.5,9.4,8.7,9.8,10.9,8.4,7.4,8.4,10,9.7,8.6,12.4,10.7,11,9.4)

We can use the following R code to get the histogram

> x1<-c(7,13.2,8.1,8.2,6,9.5,9.4,8.7,9.8,10.9,8.4,7.4,8.4,10,9.7,8.6,12.4,10.7,11,9.4)

> hist(x1,main="Histogram a)")

The result is on the first figure attached.

For this case the histogram is not too skewed and we can say that is approximately symmetrical so then we can conclude that this dataset is similar to a normal distribution

(b)data = c(2.5,1.8,2.6,-1.9,1.6,2.6,1.4,0.9,1.2,2.3,-1.5,1.5,2.5,2.9,-0.1)

> x2<- c(2.5,1.8,2.6,-1.9,1.6,2.6,1.4,0.9,1.2,2.3,-1.5,1.5,2.5,2.9,-0.1)

> hist(x2,main="Histogram b)")

The result is on the first figure attached.

For this case the data is skewed to the left and we can't assume that we have the normality assumption.

(c)data = c(3.3,1.7,3.3,3.3,2.4,0.5,1.1,1.7,12,14.4,12.8,11.2,10.9,11.7,11.7,11.6)

> x3<-c(3.3,1.7,3.3,3.3,2.4,0.5,1.1,1.7,12,14.4,12.8,11.2,10.9,11.7,11.7,11.6)

> hist(x3,main="Histogram c)")

The result is on the first figure attached.

This last case the histogram is not symmetrical and the data seems to be skewed.

7 0
3 years ago
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