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Ksenya-84 [330]
3 years ago
5

a rectangle has a hight to width ratio of 3:4.5 Give two examples of dimensions for rectangle that could be scaled versions of t

his rectangle​
Mathematics
1 answer:
Free_Kalibri [48]3 years ago
8 0

Answer:

vb n bf dgmhumrynwtfvt

Step-by-step explanation:

vb hgetyhrtewr34y4j4htredwed5g

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Given the function ƒ(x )=x^2 - 5, find ƒ(7).<br><br> 49 <br><br> 2 <br><br> 44
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Substitute the 7 in anywhere there is an x.
49-5=44
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Scientists in Florida are measuring the masses of sea turtles as they hatch. They create a line graph and plots the masses of 18
NeTakaya

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36 3/4

Step-by-step explanation:

12 1/4 + 12 1/4 + 12 1/4 = 36 3/4 :)

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What are the 3 different forms of a quadratic function and what do the variables in each represent?
Likurg_2 [28]

Answer:

general, factored, and vertex form.

general: y=ax^2+b^2+c

Factored: y=(x+a)(x+b)

Vertex: y=a(x-h)^2+k

Step-by-step explanation:

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3 years ago
(Kinda need help ASAP)
denis23 [38]

Answer:

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4 0
2 years ago
Suppose each of the following data sets is a simple random sample from some population. For each dataset, make a normal QQ plot.
adell [148]

Answer:

a) For this case the histogram is not too skewed and we can say that is approximately symmetrical so then we can conclude that this dataset is similar to a normal distribution

b) For this case the data is skewed to the left and we can't assume that we have the normality assumption.

c) This last case the histogram is not symmetrical and the data seems to be skewed.

Step-by-step explanation:

For this case we have the following data:

(a)data = c(7,13.2,8.1,8.2,6,9.5,9.4,8.7,9.8,10.9,8.4,7.4,8.4,10,9.7,8.6,12.4,10.7,11,9.4)

We can use the following R code to get the histogram

> x1<-c(7,13.2,8.1,8.2,6,9.5,9.4,8.7,9.8,10.9,8.4,7.4,8.4,10,9.7,8.6,12.4,10.7,11,9.4)

> hist(x1,main="Histogram a)")

The result is on the first figure attached.

For this case the histogram is not too skewed and we can say that is approximately symmetrical so then we can conclude that this dataset is similar to a normal distribution

(b)data = c(2.5,1.8,2.6,-1.9,1.6,2.6,1.4,0.9,1.2,2.3,-1.5,1.5,2.5,2.9,-0.1)

> x2<- c(2.5,1.8,2.6,-1.9,1.6,2.6,1.4,0.9,1.2,2.3,-1.5,1.5,2.5,2.9,-0.1)

> hist(x2,main="Histogram b)")

The result is on the first figure attached.

For this case the data is skewed to the left and we can't assume that we have the normality assumption.

(c)data = c(3.3,1.7,3.3,3.3,2.4,0.5,1.1,1.7,12,14.4,12.8,11.2,10.9,11.7,11.7,11.6)

> x3<-c(3.3,1.7,3.3,3.3,2.4,0.5,1.1,1.7,12,14.4,12.8,11.2,10.9,11.7,11.7,11.6)

> hist(x3,main="Histogram c)")

The result is on the first figure attached.

This last case the histogram is not symmetrical and the data seems to be skewed.

7 0
3 years ago
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