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kogti [31]
3 years ago
8

If 50-3x=x-26, then x=

Mathematics
1 answer:
IgorLugansk [536]3 years ago
6 0

Answer:

x=19

Step-by-step explanation:

50 - 3x = x -26

50-3x-50=x-26-50

-3x=x-76

-3x-x=x-76-x

-4x= -76

x= -76/-4

x=19

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never [62]

Answer:

2

Step-by-step explanation:

Don't really know how to explain it. OmniCalculator is very helpful.

Hope that this helps!

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3 years ago
F(x)=3x/x-1<br><br> answer needed
Ray Of Light [21]
Depends on what your answering for, for this i’d assume ur answering for the function which the answer would be 3x/x-1 - f(x) = 0. let me know if it’s something else you are trying to solve for
5 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
WILL MARK BRAINLIEST!!
Snowcat [4.5K]
15/10 = 18-x/x is the ratio of the larger to the smaller triangle

Cross multiply:
15x = 10(18 - x)
15x = 180 - 10x
25x = 180
x = 7.2

Plug back in to check
15/10 = 1.5

18-7.2/7.2 = 10.8/7.2 = 1.5 :)
6 0
3 years ago
Helllllllllpppp !!!!!!!!
AURORKA [14]
O = x, h=15 angle = 23
sin 23 = o/h
sin 23 x 15 = x
x=5.9 --> C
4 0
3 years ago
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