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e-lub [12.9K]
3 years ago
12

Lil makes $3.51 am hour more than Noah. Both of them worked for 42 hours on a project. Together, they made $1147.02 what is Noah

’s hourly wage?
Mathematics
1 answer:
finlep [7]3 years ago
8 0

Answer:

999.60

Step-by-step explanation:

$3.51 times 42 is 147.42 and 1147.02 minus 147.42 is $999.60

TBH this might not be right I'm not great at math buuut- heeeyy

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Which describes the correlation shown in the scatterplot?
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Answer:

C.There is no positive or negative correlation.

Step-by-step explanation:

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Ian wants to run 412 miles to get ready for his upcoming race. He has set up 3 markers the same distance apart to show how far h
Salsk061 [2.6K]

Answer:

Each marker will be exactly <u>103</u> miles apart.

Step-by-step explanation:

It is given that Ian wants to run 412 miles and has set up 3 markers the same distance apart.

Between the start and end of run, the 3 separate markers will divide the 412-mile distance into 4 equal segments.

So each segment = 412/4 = 103 miles

Each marker will be exactly <u>103</u> miles apart.


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Find the volume of the figure. Round to nearest hundredth. *
hoa [83]

Answer:

6 mi

Step-by-step explanation:

I think so it 6mi I need help

8 0
3 years ago
Let X and Y be discrete random variables. Let E[X] and var[X] be the expected value and variance, respectively, of a random vari
Ulleksa [173]

Answer:

(a)E[X+Y]=E[X]+E[Y]

(b)Var(X+Y)=Var(X)+Var(Y)

Step-by-step explanation:

Let X and Y be discrete random variables and E(X) and Var(X) are the Expected Values and Variance of X respectively.

(a)We want to show that E[X + Y ] = E[X] + E[Y ].

When we have two random variables instead of one, we consider their joint distribution function.

For a function f(X,Y) of discrete variables X and Y, we can define

E[f(X,Y)]=\sum_{x,y}f(x,y)\cdot P(X=x, Y=y).

Since f(X,Y)=X+Y

E[X+Y]=\sum_{x,y}(x+y)P(X=x,Y=y)\\=\sum_{x,y}xP(X=x,Y=y)+\sum_{x,y}yP(X=x,Y=y).

Let us look at the first of these sums.

\sum_{x,y}xP(X=x,Y=y)\\=\sum_{x}x\sum_{y}P(X=x,Y=y)\\\text{Taking Marginal distribution of x}\\=\sum_{x}xP(X=x)=E[X].

Similarly,

\sum_{x,y}yP(X=x,Y=y)\\=\sum_{y}y\sum_{x}P(X=x,Y=y)\\\text{Taking Marginal distribution of y}\\=\sum_{y}yP(Y=y)=E[Y].

Combining these two gives the formula:

\sum_{x,y}xP(X=x,Y=y)+\sum_{x,y}yP(X=x,Y=y) =E(X)+E(Y)

Therefore:

E[X+Y]=E[X]+E[Y] \text{  as required.}

(b)We  want to show that if X and Y are independent random variables, then:

Var(X+Y)=Var(X)+Var(Y)

By definition of Variance, we have that:

Var(X+Y)=E(X+Y-E[X+Y]^2)

=E[(X-\mu_X  +Y- \mu_Y)^2]\\=E[(X-\mu_X)^2  +(Y- \mu_Y)^2+2(X-\mu_X)(Y- \mu_Y)]\\$Since we have shown that expectation is linear$\\=E(X-\mu_X)^2  +E(Y- \mu_Y)^2+2E(X-\mu_X)(Y- \mu_Y)]\\=E[(X-E(X)]^2  +E[Y- E(Y)]^2+2Cov (X,Y)

Since X and Y are independent, Cov(X,Y)=0

=Var(X)+Var(Y)

Therefore as required:

Var(X+Y)=Var(X)+Var(Y)

7 0
3 years ago
Please help me answer this!!
tangare [24]

Answer:

Here,

1-4

2-3

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