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Reil [10]
3 years ago
14

PLEASE HELP ME! Tysm <3 Have a great day. The Picture is below.

Mathematics
2 answers:
uysha [10]3 years ago
8 0

Answer:

All three are false.

Step-by-step explanation:

Let's say a equaled 2 and b equaled 5.

For reference: |2| = 2 |5| = 5

2 > 5 - False

|2| > |5| - False

2 > |5| - False

Hope this helps!!

kykrilka [37]3 years ago
7 0

Answer:

1.false

2.false

3.false

You might be interested in
What 6 ways can you write 835,000
igomit [66]
Here are the 6 ways to write 835 000
1st way = word form way
=> eight hundred thirty five thousands.
2nd way = place value form
=> 8 hundred thousand 3 ten thousand 5 thousand
3rd way = expanded form
=> 800 000 + 30 000 + 5 000
4th way = algebraic form
=> 835 000
5th way = numeric addition form
=> 800 000 + 35 000
6th way = fraction form
=> 835 000 / 1






8 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
A sample of men's heights was taken and the mean was 68.8 inches. The standard deviation is 2.8 inches. What percent of the men
jarptica [38.1K]

Answer:

87.29%

Step-by-step explanation:

Given: Mean= 68.8 inches

           Standard deviation= 2.8 inches

           

Now, finding the percent of the men in the sample were greater than 72 inches.

We know, z-score= \frac{x-mean}{standard\ deviation}

z-score= \frac{72-68.8}{2.8}

⇒ z-score= \frac{3.2}{2.8} = 1.14

∴ z-score= 1.14

Next, using normal distribution table to find percentage.

Coverting 0.8729 into percentage= 0.8729\times 100

We get the percentage as 87.29%

Hence, 87.29% of the men in the sample were greater than 72 inches.

6 0
3 years ago
I need help with this question
beks73 [17]

Answer:

  1024

Step-by-step explanation:

1 division: 2

2 divisions: 2×2 = 4

3 divisions: 2×(2×2) = 8

The number of viruses is 2^n, where n is the number of divisions.

After 10 divisions, there are 2^10 = 1024 viruses.

4 0
2 years ago
Help me pls this is math and im so confused ​
EleoNora [17]
9.08+x=25.08
-9.08 -9.08
x=16
3/4x=12
x4/3 x4/3
x=16

4.5x=72
/4.5 /4.5
x=16

Both
8 0
2 years ago
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