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Dominik [7]
3 years ago
7

Drag each number to the correct location on the table. Each number can be used more than once, but not all numbers will be used.

Mathematics
1 answer:
muminat3 years ago
8 0

Answer:

1. 12x^2-13.4x-11

- Degree: 2

- Number of terms: 3

2. 7x^3+168

 - Degree: 3

- Number of terms: 2

3. 9x^4-9

- Degree: 4

- Number of terms: 2

Step-by-step explanation:

For this exercise you need to remember the multiplication of signs:

(+)(+)=+\\(-)(-)=+\\(-)(+)=-\\(+)(-)=-

1. Given:

(4x + 2.2)(3x - 5)

Apply the Distributive property:

=12x^2+6.6x-20x-11

Add the like terms:

=12x^2-13.4x-11

You can idenfity that:

- Degree: 2

- Number of terms: 3

2. Given:

(-304 +503 - 12) + (7x^3 - 25 + 6)

Add the like terms:

=187 + 7x^3 - 25 + 6=7x^3+168

You can idenfity that:

- Degree: 3

- Number of terms: 2

3. Given:

(3x^2 - 3)(3x^2 + 3)

Apply Distributive property:

=9x^4-9x^2+9x^2-9

Add the like terms:

=9x^4-9

You can idenfity that:

- Degree: 4

- Number of terms: 2

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Tony has a hosepipe. The length of the hosepipe is 20 m. Tony stores the hosepipe on a reel. The weight of the reel is 1.4 kg. 1
harina [27]

Answer:

the total weight of the hosepipe and the reel is 7.4 kg

Step-by-step explanation:

First, we need to find the weight of the hosepipe, we know that 1/2 metre of the hosepipe has a weight of 150 grams, therefore we can conclude that 1 meter has a weight of (150)(2) = 300 grams.

If one meter weighs 300 grams, then 20 meters have a weight of 20(300) = 6000 grams. But since 1000 grams are 1 kg, this would be equivalent to 6 kg.

Now, to find the total weight of the hosepipe and the reel we are going to sum up the 2 weights:

Total weight = hose pipe weight + reel weight

Total weight = 6 kg + 1.4 kg

Total weight = 7.4 kg.

Therefore, the total weight of the hosepipe and the reel is 7.4 kg

8 0
3 years ago
Which recursive sequence would produce the sequence 8,-35,137,…?
vagabundo [1.1K]

The recursive sequence that would produce the sequence 8,-35,137,… is T(n + 1) = -3 - 4T(n) where T(1) = 8

<h3>How to determine the recursive sequence that would produce the sequence?</h3>

The sequence is given as:

8,-35,137,…

From the above sequence, we can see that:

The next term is the product of the current term and -4 added to -3

i.e.

Next term = -3 + Current term * -4

So, we have:

T(n + 1) = -3 + T(n) * -4

Rewrite as:

T(n + 1) = -3 - 4T(n)

Hence, the recursive sequence that would produce the sequence 8,-35,137,… is T(n + 1) = -3 - 4T(n) where T(1) = 8

Read more about recursive sequence at

brainly.com/question/1275192

#SPJ1

4 0
1 year ago
Help please I need help :(
maks197457 [2]

A = 1 and 8

B = 2 and 4

C = 2 and 7

I’m pretty sure this is right? I’m still learning too :p

5 0
3 years ago
Read 2 more answers
What is the same as moving the decimal point 3 places to the right in a decimal point.
ladessa [460]

Answer:

5

Step-by-step explanation:

l

4 0
3 years ago
Read 2 more answers
Let X1 and X2 be independent random variables with mean μand variance σ².
My name is Ann [436]

Answer:

a) E(\hat \theta_1) =\frac{1}{2} [E(X_1) +E(X_2)]= \frac{1}{2} [\mu + \mu] = \mu

So then we conclude that \hat \theta_1 is an unbiased estimator of \mu

E(\hat \theta_2) =\frac{1}{4} [E(X_1) +3E(X_2)]= \frac{1}{4} [\mu + 3\mu] = \mu

So then we conclude that \hat \theta_2 is an unbiased estimator of \mu

b) Var(\hat \theta_1) =\frac{1}{4} [\sigma^2 + \sigma^2 ] =\frac{\sigma^2}{2}

Var(\hat \theta_2) =\frac{1}{16} [\sigma^2 + 9\sigma^2 ] =\frac{5\sigma^2}{8}

Step-by-step explanation:

For this case we know that we have two random variables:

X_1 , X_2 both with mean \mu = \mu and variance \sigma^2

And we define the following estimators:

\hat \theta_1 = \frac{X_1 + X_2}{2}

\hat \theta_2 = \frac{X_1 + 3X_2}{4}

Part a

In order to see if both estimators are unbiased we need to proof if the expected value of the estimators are equal to the real value of the parameter:

E(\hat \theta_i) = \mu , i = 1,2

So let's find the expected values for each estimator:

E(\hat \theta_1) = E(\frac{X_1 +X_2}{2})

Using properties of expected value we have this:

E(\hat \theta_1) =\frac{1}{2} [E(X_1) +E(X_2)]= \frac{1}{2} [\mu + \mu] = \mu

So then we conclude that \hat \theta_1 is an unbiased estimator of \mu

For the second estimator we have:

E(\hat \theta_2) = E(\frac{X_1 + 3X_2}{4})

Using properties of expected value we have this:

E(\hat \theta_2) =\frac{1}{4} [E(X_1) +3E(X_2)]= \frac{1}{4} [\mu + 3\mu] = \mu

So then we conclude that \hat \theta_2 is an unbiased estimator of \mu

Part b

For the variance we need to remember this property: If a is a constant and X a random variable then:

Var(aX) = a^2 Var(X)

For the first estimator we have:

Var(\hat \theta_1) = Var(\frac{X_1 +X_2}{2})

Var(\hat \theta_1) =\frac{1}{4} Var(X_1 +X_2)=\frac{1}{4} [Var(X_1) + Var(X_2) + 2 Cov (X_1 , X_2)]

Since both random variables are independent we know that Cov(X_1, X_2 ) = 0 so then we have:

Var(\hat \theta_1) =\frac{1}{4} [\sigma^2 + \sigma^2 ] =\frac{\sigma^2}{2}

For the second estimator we have:

Var(\hat \theta_2) = Var(\frac{X_1 +3X_2}{4})

Var(\hat \theta_2) =\frac{1}{16} Var(X_1 +3X_2)=\frac{1}{4} [Var(X_1) + Var(3X_2) + 2 Cov (X_1 , 3X_2)]

Since both random variables are independent we know that Cov(X_1, X_2 ) = 0 so then we have:

Var(\hat \theta_2) =\frac{1}{16} [\sigma^2 + 9\sigma^2 ] =\frac{5\sigma^2}{8}

7 0
3 years ago
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