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bixtya [17]
4 years ago
7

Lines a and b are parallel. What is the value of x? 1) 15 2) 16 3) 56 4) 64

Mathematics
1 answer:
omeli [17]4 years ago
6 0

Answer:

2) 16

Step-by-step explanation:

180 - 124 = 56

180 - 56 - 60 = 64

64 ÷ 4 = 16

x = 16

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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
4 years ago
What percent of 67 is 33? Round to the nearest tenth of a percent
julia-pushkina [17]
We will make a proportion:
67 : 33 = 100 : x
67 x = 33*100
67 x= 3300
x= 3300/97= 49.2537
Round to the nearest tenth:
Answer: 49.3%.
3 0
3 years ago
What is 40% of 400<br> Show work
kirill [66]
40% of 400 would be 160.
40% is 0.4 
so
0.4*400 = 160
to prove my work
400/160 = 0.4 = 40%
6 0
3 years ago
Read 2 more answers
Express 1 as a percentage
diamong [38]

Answer:

1 as a percentage is 100%

6 0
3 years ago
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vodka [1.7K]
The answer is 104.7782 tell me if it’s right
7 0
3 years ago
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