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Svetlanka [38]
3 years ago
11

Need help with number 40 please !

Mathematics
1 answer:
zhenek [66]3 years ago
5 0

Answer: The answers are 4,100+400+3,100+2,100=9,700 the next row is 4,900+1,200+3,900+2,900=12,900


Step-by-step explanation:


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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
3 years ago
Simplify (x^4-4x^3+9x^2-5x) (x^3-3x^2+7) -30x^2+25x^4
Masja [62]

Answer:

x^7-7x^6+21x^5-13x^3+33x^2-35x

Step-by-step explanation:

there you go!

7 0
2 years ago
Write 8% as a decimal
viva [34]

Answer:

0.08

Step-by-step explanation:

4 0
3 years ago
What is the slope of the function below?<br> y=3x+5 please help!!!
ladessa [460]
X=-5/3 it’s a fraction
8 0
3 years ago
Which table represents exponential growth? A 2-column table has 4 rows. The first column is labeled x with entries 1, 2, 3, 4. T
Papessa [141]

Answer:

Table 2

Step-by-step explanation:

We have the tables:

<u>Table 1:</u>

x: 1  2  3  4

y: 2  4  6  8

<u>Table 2:</u>

x: 1  2  3  4

y: 2  4  8  16

<u>Table 3:</u>

x: 1  2  3  4

y: 2  4  7  11

<u>Table 4:</u>

x: 1  2  3  4

y: 2  4  6  10

An exponential growth data set will show a common ratio between y values. Let's look at each of the ratios from each table.

<u>Table 1:</u>

8/6 = 4/3

6/4 = 3/2

Already, we can see that 4/3 ≠ 3/2, which means that this doesn't have a common ratio. So Table 1 is wrong.

<u>Table 2:</u>

16/8 = 2

8/4 = 2

4/2 = 2

The common ratio here is 2, so we know this is correct.

<u>Table 3:</u>

11/7 = 1.57

7/4 = 1.75

Again, we can see that 1/57 ≠ 1.75, so this is wrong.

<u>Table 4:</u>

10/6 = 1.67

6/4 = 1.5

Again, there is no common ratio here, so this is wrong.

The answer is thus Table 2.

8 0
3 years ago
Read 2 more answers
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