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Allushta [10]
3 years ago
13

CAN SOMEONE PLS HELP ME WITH THIS. YOU'LL EARN 50 POINTS!!!

Mathematics
2 answers:
guapka [62]3 years ago
7 0

Answer: W gives the weight of a puppy, in pounds, as a

function of its age, t, in months.

Step-by-step explanation:

melomori [17]3 years ago
4 0

Answer:

It A w(2)=5

Step-by-step explanation:

You see

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Use
Brums [2.3K]

Answer:

I don't know what inductive reasoning is but, the next two numbers in the pattern are -243 and +729

Step-by-step explanation:

each number is being multiplied by a factor of (-3).

8 0
2 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
Match the sides based on their relationship to A<br><br> PLEASE ANSWER ASAP
TiliK225 [7]

Answer:

13 hypotenuse

12 adjacent

5 opposite

5 0
3 years ago
Read 2 more answers
CAN SOMEONE GIVE ME THE REAL ASNWER TO THIS IM IN A RUSH
rosijanka [135]

Answer:

145 degrees

Explanation:

A straight line is 180 degrees so minus 180 by 35 to get 145 degrees.

8 0
2 years ago
Read 2 more answers
Solve for e.<br> 0.75(8 + e) = 2 - 1.25e
timofeeve [1]

Answer:

e = -2

Step-by-step explanation:

Well to solve for e in the following equation,

.75(8 + e) = 2 - 1.25e

We need to distribute and use the communicative property to find <em>e</em>.

6 + .75e = 2 - 1.25e

-2 to both sides

4 + .75e = -1.25e

-.75 to both sides

4 = -2e

-2 to both sides

e = -2

<em>Thus,</em>

<em>e is -2.</em>

<em />

<em>Hope this helps :)</em>

3 0
3 years ago
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