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2 years ago

PLZ HELP ASAP LAST SIMMILAR TRIANGLES

Mathematics

2 answers:

igor_vitrenko [27]2 years ago

8 0

QR/KL=PQ/JK=PR/JL
t/1.5=25/5=22.5/4.5
t/1.5=5=5
t/1.5=5
solving for t:
1.5(t/1.5)=1.5(5)
t=7.5

Answer: t=7.5

Viktor [21]2 years ago

7 0

By similarity of triangles we have the following relationship:

(t) / (22.5) = (1.5) / (4.5)

Clearing the value of t we have:

t = ((1.5) / (4.5)) * 22.5

Simplifying we have:

t = ((1) / (3)) * 22.5

The value of t is:

T = 7.5

Answer:

T = 7.5

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The range of the given graph is [5, ∞).

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Range refers the set of possible output values, and if we have the graph, then the set of all possible y values on the graph is defined as the range.

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Here we have the graph.

Through the given graph we have to find the range of the line.

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7 months ago

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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.